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Since training data for the detection task is often not available due to the diversity of generative models, training-free detection approaches have been practically considered. A common approach is to utilize the image-level reconstruction error from the latent diffusion model (LDM). However, we find this score suffers from instance-specific biases, particularly in images with simple backgrounds. To this end, we propose a novel image-level debiasing score function that cancels out background contribution by normalizing the reconstruction error on the augmented images with similar background information. To be specific, we show that rotation and low-pass filtering are effective augmentation strategies. To promote generalization to broader generative models, we newly explore latent-level reconstruction error as an additional training-free signal. However, we observe that the latent-level score also suffers to latent-specific bias. 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"publication_type": "conference", "abstract": "The rapid proliferation of AI-generated images has raised growing concerns about content authenticity and accountability. Existing image forensic studies mainly focus on detection and attribution, aiming to determine whether an image is generated or to identify its source model. However, these approaches rely on stable and generalizable class-level evidence, and often become unreliable when generative models evolve or visual artifacts diminish. In this work, we introduce AI-generated image homology detection, a new forensic task that asks a fundamentally different question: whether two images originate from the same generative source, regardless of whether that source can be explicitly identified. Unlike detection and attribution, homology detection formulates image forensics as a relational problem rather than a categorical one, enabling forensic analysis even when absolute judgments are uncertain or unavailable. We present the first systematic study of this task. Using a generic Siamese framework, we demonstrate that homology detection is feasible across diverse generative models, including unseen GANs and diffusion models. Through extensive experiments, we analyze embedding interaction strategies, examine the transferability of detection and attribution models, and investigate robustness under realistic image perturbations. Our results reveal that naive homology detectors already establish meaningful baselines, while transferred detection and attribution models exhibit limited generalization. Moreover, we show that asymmetric data augmentation plays a critical role in improving robustness for pairwise forensic analysis. Overall, this work positions homology detection as an orthogonal and complementary capability to existing forensic tasks, and provides new insights into relational modeling for AI-generated image forensics in open and evolving web environments. 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Existing detection methods handle all image patches uniformly, overlooking that low-texture regions exhibit more pronounced artifact signals than complex-textured areas, where high-frequency information of complex texture inherent to the complex content interferes with artifact detection. To address this limitation, we proposed a single exposed patch (SEP) detection framework that strategically identifies and mines one patch per image to capture distinctive cues. Specifically, we developed an exposed patch selector (EPS) that precisely identifies the most discriminative patch region by analyzing pixel-level texture distributions, ensuring that the selected patch is minimally affected by high-frequency noise in the image. To better exploit artifact cues from the selected patch and mitigate the interference from irrelevant high-frequency information, we proposed the spatial artifact perception module (SAPM). The module preserves low-level features and spatial structure and introduces a guided bidirectional cross-spatial attention (GBCSA) mechanism guided by high-frequency cues, reducing reliance on high-frequency inputs and enhancing sensitivity to minor artifacts in flat regions. Building on this, we designed a frequency perception module (FPM) to detect the periodic artifact patterns generated in the frequency domain by generative models (e.g., GANs), thereby enhancing the ability of the model to detect artifacts across different generative mechanisms. Experimental results demonstrate the superior cross-generator generalization capabilities of SEP. We conducted comparisons on two major public datasets (GenImage and AIGCBenchmark). The million-scale GenImage dataset comprises eight different generative models, and the AIGCBenchmark encompasses a broader range of 16 diverse generative models. 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To address this, we propose Panoptic Patch Learning framework, which integrates: (1) Randomized Patch Reconstruction, injecting synthetic cues into randomly selected patches to diversify artifact recognition; (2) Patch-wise Contrastive Learning, enforcing consistent discriminative capability across patches to ensure their uniform utilization. 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Several prior methods detect AI-generated images by exploiting low-level fake artifacts left by generators. However, these fake artifacts are fragile and easily suppressed by post-processing (e.g., JPEG compression), leading detectors to misclassify fake images as real. To address this robustness limitation, we propose ArtGate, a novel AI-generated image detector that integrates a CLIP-ViT backbone with a frequency-domain artifact branch, and carefully design a confidence-aware gating mechanism to modulate the artifact branch. Specifically, the artifact branch employs wavelet analysis to extract artifact features from the high-frequency subbands of the image, while a confidence-aware gating mechanism selectively activates this branch only when generation-related fake artifacts are detected. After modulation by the gating mechanism, the artifact features are subsequently injected into the CLIP image encoder and adaptively fused with semantic features to enhance the detection performance. Extensive experiments demonstrate that ArtGate outperforms state-of-the-art methods in both generalization and robustness. 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"canonical_name": "Harbin Institute of Technology", "institution_id": "institution:fe438b1c7662476f", "institution_type": "university", "country": "China", "country_code": "", "region": "Heilongjiang", "location_display": "Heilongjiang, China" }, { "index": 2, "name": "Nanchang University", "canonical_name": "Nanchang University", "institution_id": "institution:74eb5a7db0242865", "institution_type": "university", "country": "China", "country_code": "", "region": "Jiangxi", "location_display": "Jiangxi, China" } ], "current_institution": null }, { "paper_id": "curated:db62aa9b0b1aab0c9f7a", "title": "Artifact-Aware Explainable Deepfake Detection via Fake-Only High-Frequency Alignment Supervision", "in_scope": true, "year": 2026, "publication_year": 2026, "publication_date": "", "task": "detection", "venue": "IEEE International Conference on Computing, Power, and Communication Technologies", "publisher": "", "publication_type": "conference", "abstract": "In recent years, deepfake 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Experimental outcomes states that the proposed supervision significantly increases the alignment gap to 0.262 while preserving 99.96% validation accuracy. These findings highlight the need to incorporate artifact aware supervision to improve interpretability without degrading model performance. 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Existing detectors often rely on either convolutional networks, which focus on local patterns but lack global reasoning, or Transformers, which capture long-range context but suffer from high computational cost. Recent state space models such as Mamba provide linear time processing, yet their causal structure leads to long-range dependency decay, making them less effective for detecting forgery clues that appear across distant image regions. In this work, we propose Multi-scale Linear Local Attention (MLLA), a unified framework for AIGC detection that combines local artifact modeling with efficient global context reasoning. Our design integrates an artifact-aware tokenization (AAT) with a Linear Local Attention (LLA) block that merges depthwise convolutions, linear attention, and rotary positional embedding to overcome the limitations of both Transformers and causal state space models. 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While recent methods have explored prototypical networks, CLIP-based features, frequency masking, and teacher-student frameworks, these approaches typically rely on standard supervised learning that requires retraining for new generators. We present a comprehensive study that applies episodic few-shot learning to enhance existing state-of-the-art deepfake detection methods for improved cross-generator generalization. We demonstrate the enhancement on three architectures: ResNet50 with prototypical networks, CLIP with frequency masking, and GenDet teacher-student by incorporating episodic few-shot learning. All three methods are trained exclusively on ProGAN-generated images and evaluated on multiple unseen GAN families and diffusion models in both zero-shot and few-shot settings. Our experiments demonstrate that episodic few-shot learning substantially improves the generalization capability of all baseline methods. The CLIP-based approach with frequency masking shows strong performance on diffusion models, while the enhanced GenDet shows consistent results across diverse generators. 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Typically, they usually take RGB images as input, and use backbones like ResNet, CLIP visual encoder to extract features. Even though these backbones are capable to detect fake images, they are mainly designed to extract the high-level semantic information, rather than inherently designed for fake image detection. To this end, in this paper, we want to optimize the embedding space tailored for detecting fake images via representation learning. We notice that Neighboring Pixel Relationships (NPR) is capable to capture the intrinsic forgery clues, which means that NPR may be a good input to perform representation learning that aims at learning the embedding space tailored for detecting fake images.Therefore, we leverage features from both RGB modality and NPR modality to perform two proposed representation learning methods, Cross-Modal Contrastive Learning (CMCL) and Cross-Modal Mutual Distillation (CMMD), in order to learn the forgery-aware embedding space. 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Detecting AI-generated CT images remains challenging for two key reasons: existing benchmarks cover only limited generation sources, and many detectors are adapted from natural-image forensics without explicitly modeling CT-specific imaging properties. In this paper, we introduce CTForensics, a dataset for detecting AI-generated CT images. CTForensics contains 75,990 2D CT images, including a dedicated test benchmark of 29,990 balanced authentic and generated samples from ten representative CT generative models spanning GAN-based and diffusion-based paradigms. We further propose the Enhanced Spatial-Frequency CT Forgery Detector (ESF-CTFD), a CT-oriented CNN framework built around a Wavelet-Enhanced Central Stem, Multi-Scale Spatial Aggregation, and a Frequency-Aware Prediction Block. The Wavelet-Enhanced Central Stem enhances local intensity correlations and high-frequency residuals, Multi-Scale Spatial Aggregation aligns anatomical features across resolutions with lightweight residual units, and the Frequency-Aware Prediction Block models global spectral artifacts. Extensive experiments on CTForensics show that ESF-CTFD achieves 96.01% mAcc and 99.96% mAP, outperforming existing methods and maintaining strong robustness under realistic perturbations with only a 0.99% average drop. 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Constrained by the limited semantic diversity in existing generated images and the deep entanglement of forgery artifacts with high-level semantic information in the feature space, existing methods are prone to overfitting dataset-specific semantic distributions, thereby leading to content bias. To address this limitation, we propose the Frequency-Enhanced Semantic-Agnostic Decoupling CLIP (FESA-CLIP). This framework mitigates content bias by integrating a semantic-agnostic decoupling strategy with a frequency-aware enhancement mechanism, thereby suppressing semantic interference while amplifying fine-grained artifacts within feature representations. Specifically, we design a plug-and-play FESA-Adapter module that employs a dual-subspace feature decomposition mechanism to project features into two specialized subspaces: a semantic-related one and a semantic-agnostic one. 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"publication_type": "conference", "abstract": "The remarkable realism of images generated by diffusion models poses critical detection challenges. Current methods utilize reconstruction error as a discriminative feature, exploiting the observation that real images exhibit higher reconstruction errors when processed through diffusion models. However, these approaches require costly reconstruction computations and depend on specific diffusion models, making their performance highly model-dependent. We identify a fundamental difference: real images are more difficult to fit with Gaussian distributions compared to synthetic ones. In this paper, we propose Forgery Identification via Noise Disturbance (FIND), a novel method that requires only a simple binary classifier. It eliminates reconstruction by directly targeting the core distributional difference between real and synthetic images. Our key operation is to add Gaussian noise to real images during training and label these noisy versions as synthetic. This step allows the classifier to focus on the statistical patterns that distinguish real from synthetic images. We theoretically prove that the noise-augmented real images resemble diffusion-generated images in their ease of Gaussian fitting. Furthermore, simply by adding noise, they still retain visual similarity to the original images, highlighting the most discriminative distribution-related features. The proposed FIND improves performance by 11.7% on the GenImage benchmark while running 126x faster than existing methods. 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This is particularly concerning for developing nations like Bangladesh. A massive number of AI-generated images are circulating on social media, fueling misinformation and sometimes triggering social conflict or personal harassment. Advanced models such as GANs, Diffusion and Transformer-based systems are generating increasingly realistic visuals, often indistinguishable from genuine photos. Moreover, rapid evolution of gen-AI is weakening the reliability of existing detection systems by eliminating generator-specific artifacts. Existing detectors achieve high indistribution accuracy, but suffer catastrophic degradation (often >30%) on unseen generators. We propose Forensic Invariant Learning (FIL), a detection framework grounded in generatoragnostic forensic signals. A six-model stacking ensemble with a logistic regression meta-learner is combined with a conservative physics-informed gating mechanism calibrated at the 95th percentile of the natural-image distribution. Evaluated on eight generators with over 38,000 images, FIL achieves 85.4% accuracy (AUC =0.933) on the combined test set and 74.1% mean LOGO accuracy across all eight generator families, demonstrating robustness to unseen architectures. Statistical significance is confirmed via McNemar's test (p<0.05 against 4 of 5 baselines), Wilcoxon signed-rank test and 2,000 iteration bootstrap confidence intervals (95%) CI: [0.846,0.862]). A controlled comparison against six baseline methods on identical data splits demonstrates consistent advantages (+0.8−10.5% over baselines). A five-configuration ablation study confirms that novel features contribute +1.2% accuracy, physics features +1.6% and stacking architecture +0.9% over voting. 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Despite the substantial advances in AIGI detection, how to correct detected AI-generated images with visible artifacts and restore realistic appearance remains largely underexplored. Moreover, few existing work has established the connection between AIGI detection and artifact correction. To fill this gap, we propose GenShield, a unified autoregressive framework that jointly performs explainable AIGI detection and controllable artifact correction in a closed loop from diagnosis to restoration, revealing a mutually reinforcing relationship between these two tasks. We further introduce a Visual Chain-of-Thought based curriculum learning strategy that enables self-explained, multi-step ``diagnose-then-repair'' correction with an explicit stopping criterion. A high-quality dataset with large-scale ``artifact-restored'' pairs is also constructed alongside a unified evaluation pipeline. Extensive experiments on our correction benchmark and mainstream AIGI detection benchmarks demonstrate state-of-the-art performance and strong generalization of our method. 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Existing methods mainly follow two directions: training optimization strategies, such as debiasing learning and data-source purification, and transfer-based detection through adaptation of pretrained foundation models. However, these methods still face a fundamental trade-off between content entanglement and optimization efficiency. Foundation-model adaptation often converges quickly under strong supervision, but it may also introduce spurious correlations between semantic content and authenticity labels, leading to biased feature learning. In contrast, training optimization methods that reduce content bias are better at preserving feature purity, but they often converge slowly because they lack directed optimization guidance. To address this trade-off, we propose a Geometric-Semantic Dual-Adaptation (GSDA) framework for generalizable AI-generated image detection. Rather than simply combining geometric constraints with semantic guidance, GSDA organizes them into a unified collaborative framework: implicit geometric decoupling preserves feature purity by suppressing content interference, while explicit semantic anchoring provides authenticity-oriented guidance to improve optimization. In this way, the two components play complementary roles in balancing robust generalization and efficient adaptation. Built on a pretrained CLIP-ViT backbone, GSDA applies orthogonal SVD-based constraints to preserve semantically stable structure while reserving residual adaptation space for authenticity-related cues, and introduces authenticity-aware text guidance to supply clearer optimization direction. Experiments on UniversalFakeDetect, GenImage, and AIGIBench show that GSDA achieves strong overall performance, favorable convergence behavior, and high parameter efficiency. 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While numerous deepfake detection methods have been proposed, existing benchmarks predominantly evaluate fine-tuned models, leaving a critical gap in understanding out-of-the-box performance -- the most common deployment scenario for practitioners. We present the first comprehensive zero-shot evaluation of 16 state-of-the-art detection methods, comprising 23 pretrained detector variants (due to multiple released versions of certain detectors), across 12 diverse datasets, comprising 2.6~million image samples spanning 291 unique generators including modern diffusion models. 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Empirically, the HSIC bottleneck improves transfer between diffusion and GAN families, and HGR sustains prior accuracy while adapting to 3DGS ren\u0002ders. 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This underscores the urgent need for effective image attribution. However, existing attribution datasets are constrained by limited scale, outdated generation methods, and insufficient semantic diversity - hindering the development of robust and generalizable attribution models. To address these limitations, we introduce ImageAttributionBench, a comprehensive dataset comprising images synthesized by a wide array of advanced generative models with state-of-the-art (SOTA) architectures. Covering multiple real-world semantic domains, the dataset offers rich diversity and scale to support and accelerate progress in image attribution research. To simulate real-world attribution scenarios, we evaluate several SOTA attribution methods on ImageAttributionBench under two challenging settings: (1) training on a standard balanced split and testing on degraded images, and (2) training and testing on semantically disjoint splits. 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New diffusion, adversarial and autoregressive generators appear almost monthly, making existing watermark, classifier and inversion methods obsolete upon release. The core problem lies not in model recognition, but in the inability to adapt attribution itself. We introduce IncreFA, a framework that redefines attribution as a structured incremental learning problem, allowing the system to learn continuously as new generative models emerge. IncreFA departs from conventional incremental learning by exploiting the hierarchical relationships among generative architectures and coupling them with continual adaptation. It integrates two mutually reinforcing mechanisms: (1) Hierarchical Constraints, which encode architectural hierarchies through learnable orthogonal priors to disentangle family-level invariants from model-specific idiosyncrasies; and (2) a Latent Memory Bank, which replays compact latent exemplars and mixes them to generate pseudo-unseen samples, stabilising representation drift and enhancing open-set awareness. On the newly constructed Incremental Attribution Benchmark (IABench) covering 28 generative models released between 2022 and 2025, IncreFA achieves state-of-the-art attribution accuracy and 98.93% unseen detection under a temporally ordered open-set protocol. 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Sadiq College Women University, Bahawalpur 63100, Pakistan 2 School of Engineering, Nanfang College Guangzhou, Guangzhou 510970, China 3 Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea * Authors to whom correspondence should be addressed. Sensors 2026, 26(4), 1172; https://doi.org/10.3390/s26041172 Submission received: 22 December 2025 / Revised: 26 January 2026 / Accepted: 7 February 2026 / Published: 11 February 2026 (This article belongs to the Special Issue Secure and Resilient Solutions for CCTV, Small Sensor and IoT Device Security) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract With ongoing growth in the implementation of CCTV networks, miniature sensors, and IoT devices, the quality of captured images in terms of authenticity has become a major security issue. Through advanced editing tools and generative models, the capability now exists to perform highly advanced forgeries that fail both human perception and traditional algorithms, and especially in terms of sensor-generated content. State-of-the-art algorithms typically use a single-cue characteristic in their models to stabilize performance, including local noise statistics or structural disruption patterns, making them susceptible to varied forms of manipulation. As a solution to this issue, we have developed MultiFusion, a new forgery detection framework which combines complementary forensic cues in images: SRM-based noise residuals, hierarchical texture features based on EfficientNet-B0, and global structural relationships from a vision transformer. A special DnCNN denoising preprocessing layer represses sensor noise and maintains fine traces of tampering. 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Detectors of AI generated images typically have an accuracy rate of almost 100% when they are trained on generators they have not seen information from, but perform poorly when they are trained on generators they have not seen information from, or when they are trained on a visual subject matter they have not seen information from. This paper poses the question of which visual representations actually convey transferable evidence of synthetic image formation. Using a controlled corpus of 217,100 images spanning twelve generators from five architectural families (style-based GANs, latent diffusion, pixel-space diffusion, autoregressive transformers and closed commercial systems) and five semantic domains, in which real and generated images are matched on prompt, resolution, aspect ratio, format and compression and de-duplicated by perceptual hashing and embedding similarity, we benchmark nine representation families under a common parameter budget. 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Currently, the state-of-the-art methods often involve the use of large pre-trained vision-language models, such as CLIP. Recent research has shown that the performance of CLIP can be further improved using lightweight adaptation approaches. Adapter tuning, a simple and efficient fine-tuning method for vision-language models, has been widely used. However, current research primarily focuses on tuning visual adapters, neglecting the complementary information and correlations between cross-modal image-text pairs. In this study, we propose a multi-modal adapter structure to address these issues. Specifically, we propose two types of adapters: the implicit Multi-Modal Dual Adapters (MMDA) and the explicit Multi-Modal Fake Aware Adapter (MMFA). The MMDA incorporate a text adapter and an image adapter, forming a symmetric structure that achieves cross-modal alignment. 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Existing evaluations measure classification accuracy but overlook reasoning fidelity. We propose DeepfakeJudge, a framework for scalable reasoning supervision and evaluation, that integrates an out-of-distribution benchmark containing recent generative and editing forgeries, a human-annotated subset with visual reasoning labels, and a suite of evaluation models, that specialize in evaluating reasoning rationales without the need for explicit ground truth reasoning rationales. The Judge is optimized through a bootstrapped generator-evaluator process that scales human feedback into structured reasoning supervision and supports both pointwise and pairwise evaluation. On the proposed meta-evaluation benchmark, our reasoning-bootstrapped model achieves an accuracy of 96.2\\%, outperforming \\texttt{30x} larger baselines. The reasoning judge attains very high correlation with human ratings and 98.9\\% percent pairwise agreement on the human-annotated meta-evaluation subset. These results establish reasoning fidelity as a quantifiable dimension of deepfake detection and demonstrate scalable supervision for interpretable deepfake reasoning. Our user study shows that participants preferred the reasonings generated by our framework 70\\% of the time, in terms of faithfulness, groundedness, and usefulness, compared to those produced by other models and datasets. 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and negative social implications. The machine learning community has been engaging this problem with an extensive series of publications proposing algorithmic solutions for the detection of \"fake'', e.g. entirely generated or partially manipulated images. While there is undoubtedly some progress towards technical solutions of the problem, we argue that current and prior work is focusing too much on generative algorithms and \"fake'' data-samples, neglecting a clear definition and data collection of \"real'' images. The fundamental question \"what is a real image?'' might appear to be quite philosophical, but our analysis shows that the development and evaluation of basically all current \"fake''-detection methods is relying on only a few, quite old low-resolution datasets of \"real'' images like ImageNet. However, the technology for the acquisition of \"real'' images, aka taking photos, has drastically evolved over the last decade: Today, over 90% of all photographs are produced by smartphones which typically use algorithms to compute an image from multiple inputs (over time) from multiple sensors. Based on the fact that these image formation algorithms are typically neural network architectures which are closely related to \"fake''-image generators, we state the position that today, we need to re-think the concept of \"real'' images. The purpose of this position paper is to raise the awareness of the current shortcomings in this active field of research and to trigger an open discussion whether the detection of ``fake'' images is a sound objective at all. 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"publication_date": "", "task": "detection", "venue": "Journal of Image and Graphics", "venue_name": "Journal of Image and Graphics", "venue_type": "journal", "publisher": "", "publication_type": "journal", "abstract": "人工智能生成图像检测旨在判断图像是否由生成模型生成，是人工智能安全与治理领域一个重要研究方向。然而当前生成图像检测方法因生成模型结构的多样性、生成图像的复杂性以及对生成图像后处理操作的不确定性难以实现高效和鲁棒检测。早期的检测方法重点关注对生成对抗网络生成内容的检测。近年来，扩散模型生成图像的检测受到广泛关注，与之相关的检测方法涌现，并表现出优越性能。因此，首先对近年来的主流图像生成模型进行梳理，然后从监督范式、学习方式、检测依据、骨干网络、技术手段和可解释性多种维度对现有生成图像检测方法进行分类，并以检测依据（像素域特征、频域特征、预训练模型特征、融合特征和特定规则）作为主要划分标准，对各类研究工作的基本思想与特点进行详细阐述与综合分析。此外，列举了当前用于通用生成图像检测的基准数据集，从数据集结构和规模等方面进行综合比较，并对面向检测方法的综合评测基准进行汇总。关于评估维度，从域内准确性、域外泛化性和鲁棒性3个层面进行介绍。之后，对代表性检测方法进行横向比较，就检测结果进行分析。最后，对当前生成图像检测领域待解决的问题进行总结，并对未来的研究方向进行展望。 In the era of rapid advancements in generative artificial intelligence， the explosive growth of artificial intelligence-generated content （AIGC）， propelled by the widespread use of models such as ChatGPT and diffusion models， has given rise to significant challenges. The proliferation of false information， fraudulent content， and inappropriate remarks has severely undermined the authenticity and credibility of information dissemination， highlighting the urgent need for effective artificial intelligence-generated images （AIGI） detection. The detection of AIGI， which aims to determine whether an image is generated by a generative model， is a crucial research direction in the field of AI security and governance. Early detection methods focused on detecting the content generated by generative adversarial networks. In recent years， images generated by diffusion models have received extensive attention， and related detection methods have emerged， demonstrating superior performance. However， current detection methods still encounter difficulties in achieving high-generalization and robust detection performance due to the diversity of generative model structures， the complexity of generated images， and the uncertainty of post-processing operations on generated images. This comprehensive review aims to provide a thorough understanding of the research landscape in AIGI detection. The paper first commences with an in-depth exploration of mainstream image-generation models. Generative adversarial networks， with their adversarial training mechanism between generators and discriminators， have evolved through various architectures such as conditional GAN （CGAN）， CycleGAN， and StyleGAN， each enhancing the controllability and quality of generated images. Autoregressive models， inspired by the success of GPT-series in natural language processing， such as Image GPT and DALL-E， leverage powerful attention mechanisms for image generation. Based on the principles of diffusion and reverse diffusion processes， diffusion models have demonstrated remarkable capabilities in generating high-quality images， leading to the emergence of numerous models such as Stable Diffusion and its variants in recent years. Understanding these models is fundamental because their unique characteristics leave identifiable patterns in the generated images， which detection methods aim to identify. Subsequently， existing AIGI detection methods are systematically categorized from multiple perspectives， including supervision paradigm， learning methods， detection basis， backbone network， technical means， and interpretability. In terms of the supervision paradigm， most methods are based on supervised detection， with some incorporating additional contrastive learning losses or regularization terms to improve performance. Though less common， semi-supervised and unsupervised detection methods also contribute to the field. Regarding learning methods， eager learning-based approaches， especially those that using binary classifiers， are dominant， while lazy learning-based methods， such as those relying on the K-nearest neighbors algorithm， offer an alternative without requiring extensive training. When categorized by detection basis， methods based on pixel-domain features use characteristics such as texture， color， and reconstruction error. Frequency domain-based methods detect AIGI through the analysis of unique frequency patterns of generated images， such as the spectrum replication caused by upsampling in GAN models. Methods based on pre-trained model features leverage the powerful feature extraction capabilities of large pre-trained models such as contrastive language-image pre-training（CLIP） and Vision Transformer. Fusion feature-based methods combine different types of features， either from single-modality （e.g.， fusing pixel and frequency domain features） or multi-modality （e.g.， integrating image and text features）， to improve detection accuracy. In contrast， rule-based methods rely on differences in the sensitivity of real and generated images to certain operations for detection. According to backbone network， early studies rely on traditional machine learning models such as random forest， support vector machine， and logistic regression. Convolutional neural networks such as ResNet have later become increasingly popular due to their powerful capabilities on local features. With the advent of pre-trained large models， visual language models such as CLIP， masked autoencoder（MAE）， and large language and vision assistant（LLaVa） are increasingly being used as backbone networks， often fine-tuned with techniques such as LoRA to adapt to the detection task. Aiming to enhance the efficiency and effectiveness of detection algorithms， various technical means are employed， including incremental learning， knowledge distillation， transfer learning， ensemble learning， and few-shot learning. Additionally， data augmentation methods such as JPEG compression， Gaussian blurring， and random flipping are used to increase the diversity of training samples and improve the generalization capability of the detection model. In terms of interpretability of detection methods， several techniques have been developed. Layer-wise relevance propagation analyzes the contribution of each layer in the neural network to the final decision， helping to understand model prediction. Genetic programming optimizes the model structure to improve interpretability. Local interpretable model-agnostic explanations clarifies model’s predictions at the local level without relying on the specific model structure. Grad-CAM generates visual explanations by highlighting the important regions in the image for the model’s decision-making. Using the detection basis as the main classification criterion， a detailed introduction and analysis of the research status are presented. A detailed summary of benchmark datasets for general AIGI detection is then provided. These datasets substantially vary in structure， scale， image content， and the types of generators they cover. Some datasets are designed for specific types of images or generators. For example， several datasets （such as Forensic Synthetics） are dedicated to GAN-generated image detection， while others （such as Synthbuster） focus on detecting images generated by diffusion models. Comprehensive datasets can also be used for detecting images generated by multiple types of generators. The diversity in datasets reflects the complexity of the AIGI detection task and the need for a standardized evaluation environment. Moreover， the evaluation dimensions of detection methods， namely in-domain accuracy， out-of-domain generalization， and robustness， are introduced in detail. In-domain accuracy is measured using metrics such as accuracy， precision， recall， and average precision， which measure the performance of detection methods on images generated by generative models included in the training set. In contrast， out-of-domain generalization evaluates the performance of detection methods on unknown generative models and categories outside the training set， which is crucial with the constant emergence of new generative models. This dimension is evaluated through cross-generator， cross-category， and other types of generalization tests. Robustness assesses the capability of detection methods to resist various image post-processing operations， including JPEG compression， Gaussian blurring， and adversarial sample attacks. Furthermore， a horizontal comparison of representative detection methods is conducted， and the results show that different methods exhibit diverse generalization capabilities. The choice of backbone network and training set substantially impacts detection results. For example， some methods perform better on specific generative models， while others demonstrate higher accuracy across multiple generative models. Detection methods based on fusion features generally exhibit superior performance in terms of detection accuracy and generalization. Finally， the paper highlights the challenges and open problems in the current AIGI detection field. These challenges include the construction of large-scale unbiased datasets， because the bias in existing training data can reduce the robustness and generalization of detection models. Designing robust detection methods against physical-level attacks， such as printing and social network-based image processing in real-world scenarios， is also necessary. Moreover， creating comprehensive detection methods that can effectively handle a variety of generative models is crucial to keep up with their rapid advancements. Additionally， research on anti-forensics attacks against detection methods is essential because it can help improve the resilience of detection techniques. 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We believe this limitation is largely due to inadequate training data quality. While most research focuses on developing new algorithms, less attention is given to training data selection, despite evidence that performance can be strongly impacted by spurious correlations such as content, format, or resolution. A well-designed forensic detector should detect generator specific artifacts rather than reflect data biases. To this end, we propose B-Free, a bias-free training paradigm, where fake images are generated from real ones using the conditioning procedure of stable diffusion models. This ensures semantic alignment between real and fake images, allowing any differences to stem solely from the subtle artifacts introduced by AI generation. Through content-based augmentation, we show significant improvements in both generalization and robustness over state-of-the-art detectors and more calibrated results across 27 different generative models, including recent releases, like FLUX and Stable Diffusion 3.5. Our findings emphasize the importance of a careful dataset design, highlighting the need for further research on this topic. 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Deepfake technologies like automated generation of scripts and avatars creation are applicable on many distinct fields and industries, helping in improving and enhancing the visuals and media contents to be published and shared with the public. However, Deepfake technologies also, being widely used in various malicious activities and crime cases, like financial frauds and scams, as well as sexual abuse and harassment. Hence, Deepfake detection is essential, to reduce the problems and avoid the consequences brought by Deepfake technologies. In this work, several existing Deepfake detection approaches, techniques and methods are explored and discussed. To understand better the effects and consequences caused by Deepfake, this work includes cases studies on real-world Deepfake incidents, conducted by searching through the news information accessible through internet. 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Although existing AIGI detection techniques are generally effective, they face two issues: 1) a lack of human-verifiable explanations, and 2) a lack of generalization in the latest generation technology. To address these issues, we introduce a large-scale and comprehensive dataset, Holmes-Set, which includes the HolmesSFTSet, an instruction-tuning dataset with explanations on whether images are AI-generated, and the Holmes-DPOSet, a human-aligned preference dataset. Our work introduces an efficient data annotation method called the Multi-Expert Jury, enhancing data generation through structured MLLM explanations and quality control via cross-model evaluation, expert defect filtering, and human preference modification. In addition, we propose Holmes Pipeline, a meticulously designed three-stage training framework comprising visual expert pre-training, supervised fine-tuning, and direct preference optimization. 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Detecting such images and attributing them to their generative models (GMs) is crucial. Existing deep learning methods attempt to identify and classify GM-specific artifacts but often struggle with content-independence and generalizability. In this paper, we observe that while GMs leave unique artifacts in the frequency domain, they are coupled with the image content. Based on this observation, we propose a novel deep learning-based solution that learns input-adaptive masks to highlight GMs’ artifacts and achieve high accuracy on the synthesized image attribution task. In addition, we observed that GMs’ artifacts in the frequency domain remain intact in sub-images of the original image, and they are even retained when the images are distorted. To further improve the accuracy of the proposed solution, we leverage the characteristics of GMs artifacts in sub-images and distorted images to make our network perform more effectively. Our evaluation results show that our proposed solution outperforms other state-of-the-art methods on unseen image types, showing great generalizability.", "abstract_source": "openalex", "ai_summary": "", "doi": "10.1109/access.2025.3534829", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.1109/access.2025.3534829", "primary_url": "https://doi.org/10.1109/access.2025.3534829", "landing_page_url": "", "openalex_url": "https://openalex.org/W4406857602", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/access.2025.3534829", "authors": [ { "name": "Junbin Zhang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Yixiao Wang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Hamid Reza Tohidypour", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Panos Nasiopoulos", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "openalex", "metadata_source": "openalex", "curation_status": "manually_confirmed", "review_status": "reviewed", "needs_review": false, "notes": "", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_institutions": [ "The University of British Columbia" ], "aggregated_country_names": [ "Canada" ], "aggregated_country_codes": [ "CA" ], "aggregated_regions": [ "British Columbia" ], "aggregated_region_codes": [], "curated_mappings": [ { "mapping_id": "mapping:d2177acec383989750a4", "institution_id": "institution:94efe2a875dd4d0e", "location_id": "", "institution": "The University of British Columbia", "institution_authors": [ "Junbin Zhang", "Yixiao Wang", "Hamid Reza Tohidypour", "Panos Nasiopoulos" ], "raw_affiliation": "Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada | Department of Electrical an Computer Engineering, University of British Columbia, Vancouver, BC, Canada", "mapping_status": "active", "affiliation_order": 1 } ], "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:94efe2a875dd4d0e", "institution": "The University of British Columbia", "institution_type": "university", "authors": [ "Junbin Zhang", "Yixiao Wang", "Hamid Reza Tohidypour", "Panos Nasiopoulos" ], "mapping_source": "curated_admin", "mapping_fallback": false } ], "author_institution_indices": [ { "author": "Junbin Zhang", "institution_indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ] }, { "author": "Yixiao Wang", "institution_indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ] }, { "author": "Hamid Reza Tohidypour", "institution_indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ] }, { "author": "Panos Nasiopoulos", "institution_indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ] } ], "author_affiliation_indices": [ { "author": "Junbin Zhang", "indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ], "source": "curated_admin", "fallback": false }, { "author": "Yixiao Wang", "indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ], "source": "curated_admin", "fallback": false }, { "author": "Hamid Reza Tohidypour", "indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ], "source": "curated_admin", "fallback": false }, { "author": "Panos Nasiopoulos", "indices": [ 1 ], "institution_ids": [ "institution:94efe2a875dd4d0e" ], "source": "curated_admin", "fallback": false } ], "affiliation_review_state": "curated", "institution_source": "curated", "preliminary_affiliations": false, "resolution_notes": "Preliminary automatic affiliation evidence; 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Deepfake detection technique is developed to distinguish between real and fake images, where the existing methods typically learn classifiers in the image domain or various feature domains. However, the generalizability of deepfake detection against emerging and more advanced generative models remains challenging. In this paper, being inspired by the zero-shot advantages of Vision-Language Models (VLMs), we propose a novel approach called AntifakePrompt, using VLMs (e.g., InstructBLIP) and prompt tuning techniques to improve the deepfake detection accuracy over unseen data. We formulate deepfake detection as a visual question answering problem, and tune soft prompts for InstructBLIP to answer the real/fake information of a query image. We conduct full-spectrum experiments on datasets from a diversity of 3 held-in and 20 held-out generative models, covering modern text-to-image generation, image editing and adversarial image attacks. These testing datasets provide useful benchmarks in the realm of deepfake detection for further research. Moreover, results demonstrate that (1) the deepfake detection accuracy can be significantly and consistently improved (from 71.06% to 92.11%, in average accuracy over unseen domains) using pretrained vision-language models with prompt tuning; (2) our superior performance is at less cost of training data and trainable parameters, resulting in an effective and efficient solution for deepfake detection.", "abstract_source": "", "ai_summary": "", "doi": "10.48550/arxiv.2310.17419", "arxiv_id": "2310.17419", "arxiv_url": "https://arxiv.org/abs/2310.17419", "arxiv_year": null, "has_arxiv_version": true, "paper_url": "https://openreview.net/pdf?id=TvSkPlDTVw", "primary_url": "https://openreview.net/pdf?id=TvSkPlDTVw", "landing_page_url": "http://arxiv.org/abs/2310.17419", "openalex_url": "https://openalex.org/W4387994353", "is_arxiv_preprint": false, "url": "https://openreview.net/pdf?id=TvSkPlDTVw", "authors": [ { "name": "You-Ming Chang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Chen Yeh", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Wei-Chen Chiu", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Ning Yu", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "openalex", "needs_review": false, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review. 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However, the significant differences in such artifacts among different generative models hinder these approaches from generalizing to generators not seen during training. In this work, we build upon the key idea that the spectral distribution of real images constitutes both an invariant and highly discriminative pattern for AI-generated image detection. To model this under a self-supervised setup, we employ masked spectral learning using the pretext task of frequency reconstruction. Since generated images constitute out-of-distribution samples for this model, we propose spectral reconstruction similarity to capture this divergence. Moreover, we introduce spectral context attention, which enables our approach to efficiently capture subtle spectral inconsistencies in images of any resolution. Our spectral AI-generated image detection approach (SPAI) achieves a 5.5% absolute improvement in AUC over the previous state-of-the-art across 13 recent generative approaches, while exhibiting robustness against common online perturbations. 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Extensive experiments demonstrate its effectiveness on AI-generated images produced not only by diffusion models but also by GANs, flow-based models, and their variants.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/cvpr52734.2025.00773", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.1109/cvpr52734.2025.00773", "primary_url": "https://doi.org/10.1109/cvpr52734.2025.00773", "landing_page_url": "https://doi.org/10.1109/cvpr52734.2025.00773", "openalex_url": "https://openalex.org/W4413146215", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/cvpr52734.2025.00773", "authors": [ { "name": "Nan Zhong", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Haoyu Chen", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Yiran Xu", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Zhenxing Qian", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Xinpeng Zhang", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; 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Detecting generated images in open-world settings is particularly challenging when they originate from unknown generators, as existing methods typically rely on model-specific artifacts and require retraining on new fake data, limiting their generalization and scalability. In this work, we propose Post-hoc Distribution Alignment (PDA), a generalized and model-agnostic framework for detecting AI-generated images under unknown generative threats. Specifically, PDA reformulates detection as a distribution alignment task by regenerating test images through a known generative model. When real images are regenerated, they inherit model-specific artifacts and align with the known fake distribution. In contrast, regenerated unknown fakes contain incompatible or mixed artifacts and remain misaligned. This difference allows an existing detector, trained on the known generative model, to accurately distinguish real images from unknown fakes without requiring access to unseen data or retraining. Extensive experiments across 16 state-of-the-art generative models, including GANs, diffusion models, and commercial text-to-image APIs (e.g., Midjourney), demonstrate that PDA achieves average detection accuracy of 96.69%, outperforming the best baseline by 10.71%. Comprehensive ablation studies and robustness analyses further confirm PDA's generalizability and resilience to distribution shifts and image transformations. Overall, our work provides a practical and scalable solution for real-world AI-generated image detection where new generative models emerge continuously.", "abstract_source": "openalex", "ai_summary": "", "doi": "10.48550/arxiv.2502.10803", "arxiv_id": "2502.10803", "arxiv_url": "https://arxiv.org/abs/2502.10803", "arxiv_year": null, "has_arxiv_version": true, "paper_url": "", "primary_url": "https://openalex.org/W4407684102", "landing_page_url": "", "openalex_url": "https://openalex.org/W4407684102", "is_arxiv_preprint": true, "url": "https://openalex.org/W4407684102", "authors": [ { "name": "Li Wang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Wenyu Chen", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Xiangtao Meng", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Zheng Li", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Shanqing Guo", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "openalex", "metadata_source": "openalex", "curation_status": "manually_confirmed", "review_status": "reviewed", "needs_review": false, "notes": "", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_institutions": [ "Shandong University" ], "aggregated_country_names": [ "China" ], "aggregated_country_codes": [ "CN" ], "aggregated_regions": [ "Shandong" ], "aggregated_region_codes": [], "curated_mappings": [ { "mapping_id": "mapping:f4e33061010bb59dfc22", "institution_id": "institution:b3d626f58155d650", "location_id": "", "institution": "Shandong University", "institution_authors": [ "Li Wang", "Wenyu Chen", "Xiangtao Meng", "Zheng Li", "Shanqing Guo" ], "raw_affiliation": "School of Cyber Science and Technology, Shandong University; 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While this technological progress has captured significant interest, it has also raised concerns about the high challenge in distinguishing real images from their synthetic counterparts. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero‐shot nature of vision‐language models (VLMs). We introduce an innovative method called Bi‐LORA that leverages VLMs, combined with low‐rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model‐generated images. The pivotal conceptual shift in our methodology revolves around reframing binary classification as an image captioning task, leveraging the distinctive capabilities of cutting‐edge VLM, notably bootstrapping language image pre‐training (BLIP)2. Rigorous and comprehensive experiments are conducted to validate the effectiveness of our proposed approach, particularly in detecting unseen diffusion‐generated images from unknown diffusion‐based generative models during training, showcasing robustness to noise, and demonstrating generalisation capabilities to GANs. The experiments show that Bi‐LORA outperforms state of the art models in cross‐generator tasks because it leverages multi‐modal learning, open‐world visual knowledge, and benefits from robust, high‐level semantic understanding. By combining visual and textual knowledge, it can handle variations in the data distribution (such as those caused by different generators) and maintain strong performance across different domains. Its ability to transfer knowledge, robustly extract features and perform zero‐shot learning also contributes to its generalisation capabilities, making it more adaptable to new generators. The experimental results showcase an impressive average accuracy of 93.41% in synthetic image detection on unseen generation models. 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The results reveal that these methods can effectively detect fake images and associate them with their respective generation models, achieving impressive accuracy rates ranging from 98.00% to 99.87% on our custom dataset, “DeepGuardDB”. 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To avoid the spread of misinformation and identify synthetic content, research on synthetic image detection becomes urgent. Unfortunately, limited to the singular forensic perspective, existing methods struggle to explore sufficient traces encountered with diverse synthetic techniques. In response to this, we argue that different synthetic images encompass a variety of forensic traces, and utilizing multiple experts to explore traces from diverse perspectives will be beneficial. Accordingly, a novel detector with the Mixture of multiple forensic Experts is proposed, named Forensic-MoE. To integrate multiple experts and enhance the knowledge interaction, Forensic-MoE follows an adapter-backbone architecture. Specifically, multiple adapters trained on different synthetic images serve as the trace exploration experts, and they are uniformly integrated into a pretrained backbone model to learn the detection prior and encourage the expert interaction. By guiding multiple experts to align with each other and collaborate, Forensic-MoE can integrate comprehensive and discriminative detection traces from multiple perspectives. Moreover, for the discrimination improvement of each expert, a multi-stage structure is proposed for efficient trace perception, and a patch decentralization strategy is applied to encourage the model's attention on every local region. 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Image Detection and Localization (FIDL) is highly fragmented, encompassing four domains: deepfake detection (Deepfake), image manipulation detection and localization (IMDL), artificial intelligence-generated image detection (AIGC), and document image manipulation localization (Doc). Although individual benchmarks exist in some domains, a unified benchmark for all domains in FIDL remains blank. The absence of a unified benchmark results in significant domain silos, where each domain independently constructs its datasets, models, and evaluation protocols without interoperability, preventing cross-domain comparisons and hindering the development of the entire FIDL field. To close the domain silo barrier, we propose ForensicHub, the first unified benchmark & codebase for all-domain fake image detection and localization. Considering drastic variations on dataset, model, and evaluation configurations across all domains, as well as the scarcity of open-sourced baseline models and the lack of individual benchmarks in some domains, ForensicHub: i) proposes a modular and configuration-driven architecture that decomposes forensic pipelines into interchangeable components across datasets, transforms, models, and evaluators, allowing flexible composition across all domains; ii) fully implements 10 baseline models (3 of which are reproduced from scratch), 6 backbones, 2 new benchmarks for AIGC and Doc, and integrates 2 existing benchmarks of DeepfakeBench and IMDLBenCo through an adapter-based design; iii) establishes an image forensic fusion protocol evaluation mechanism that supports unified training and testing of diverse forensic models across tasks; iv) conducts indepth analysis based on the ForensicHub, offering 8 key actionable insights into FIDL model architecture, dataset characteristics, and evaluation standards. Specifically, ForensicHub includes 4 forensic tasks, 23 datasets, 42 baseline models, 6 backbones, 11 GPU-accelerated pixel- and image-level evaluation metrics, and realizes 16 kinds of cross-domain evaluations. ForensicHub represents a significant leap forward in breaking the domain silos in the FIDL field and inspiring future breakthroughs. 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This can pose a threat to the public through a simplified spread\\nof disinformation. Machine detectors and human media expertise can help to\\ndifferentiate between AI-generated (fake) and real images and counteract this\\ndanger. Although AI generation models are highly prompt-dependent, the impact\\nof the prompt on the fake detection performance has rarely been investigated\\nyet. This work therefore examines the influence of the prompt's level of detail\\non the detectability of fake images, both with an AI detector and in a user\\nstudy. For this purpose, we create a novel dataset, COCOXGEN, which consists of\\nreal photos from the COCO dataset as well as images generated with SDXL and\\nFooocus using prompts of two standardized lengths. Our user study with 200\\nparticipants shows that images generated with longer, more detailed prompts are\\ndetected significantly more easily than those generated with short prompts.\\nSimilarly, an AI-based detection model achieves better performance on images\\ngenerated with longer prompts. 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Current synthetic image detection (SID) pipelines are primarily dedicated to crafting universal artifact features, accompanied by an oversight about SID training paradigm. In this paper, we re-examine the SID problem and identify two prevalent biases in current training paradigms, i.e., weakened artifact features and overfitted artifact features. Meanwhile, we discover that the imaging mechanism of synthetic images contributes to heightened local correlations among pixels, suggesting that detectors should be equipped with local awareness. In this light, we propose SAFE, a lightweight and effective detector with three simple image transformations. Firstly, for weakened artifact features, we substitute the down-sampling operator with the crop operator in image pre-processing to help circumvent artifact distortion. Secondly, for overfitted artifact features, we include ColorJitter and RandomRotation as additional data augmentations, to help alleviate irrelevant biases from color discrepancies and semantic differences in limited training samples. Thirdly, for local awareness, we propose a patch-based random masking strategy tailored for SID, forcing the detector to focus on local regions at training. Comparative experiments are conducted on an open-world dataset, comprising synthetic images generated by 26 distinct generative models. Our pipeline achieves a new state-of-the-art performance, with remarkable improvements of 4.5% in accuracy and 2.9% in average precision against existing methods. 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Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time pre-processing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of pre-processing, highlighting the need for more robust detection strategies. 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In this paper, we propose a novel multi-modal fusion network that leverages complementary texture and content information to improve the detection of AI-generated images. Our approach integrates three input branches: the original RGB image, a local binary pattern (LBP) map to capture micro-texture irregularities, and a gray-level co-occurrence matrix (GLCM) representation to encode statistical texture dependencies. These three streams are processed in parallel through a shared-weight convolutional backbone and subsequently fused at the feature level to enhance discrimination capability. Extensive experiments conducted on benchmark datasets demonstrate that our method outperforms existing single-modality baselines and achieves strong generalization across multiple types of generative models. 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To identify the truthfulness of generated images, most of the existing methods typically apply Fast Fourier Transform (FFT) for frequency extraction. An existing problem is that the frequency-domain representations extracted by FFT are not comprehensive for AI-generated image detection. In this paper, we propose a Multi-perspective Frequency Domain Learning (MFDL) framework, which aims to learn both generalized and discriminative frequency representations via DWT and FFT. Specifically, we design a Frequency Representation Enhancement (FRE) module using the Discrete Wavelet Transform (DWT) and incorporating a multi-granularity enhancement strategy that amplifies all subbands across high frequency to improve discriminability. Additionally, we introduce a Frequency Representation Consistency (FRC) module, which employs complex convolution to capture and preserve forgery patterns in the real and imaginary components derived from FFT. By integrating complementary frequency representations from the DWT and FFT domains obtained through the FRE and FRC modules, MFDL achieves a comprehensive understanding of forgery traces in the frequency domain. This enhances the model’s generalization capability for detecting generated content. Extensive experiments conducted on 32 distinct datasets, covering both GAN-generated and Diffusion-based images, demonstrate the effectiveness of our proposed MFDL framework. 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Although multimodal large language models (LLMs) possess advanced visual understanding capabilities, their effectiveness in distinguishing synthetic images from real ones requires systematic evaluation. This paper investigates the ability of four prominent LLMs—GPT-4V, Gemini, LLaVA, and Claude to detect AI-generated images. Using a case study methodology, twelve diverse synthetic images were presented to the models using ten distinct prompts, ranging from generic classification requests to detailed, forensically-guided queries. The study analyzes the accuracy and response patterns of each LLM, with a particular focus on the impact of prompt specificity and iterative refinement (prompt engineering) on detection performance. The results indicate that while GPT-4V demonstrated superior consistency, the performance of all tested LLMs, particularly Gemini, Claude, and LLaVA, was significantly influenced by prompt quality. 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This progress presents significant challenges for digital content authentication. Furthermore, deepfake technologies are being actively exploited in cybersecurity attacks, introducing serious risks to information security. The use of hyper-realistic synthetic media in social engineering dramatically enhances the effectiveness of attacks such as identity theft and ransomware. Existing Deep Learning (DL)-based detectors often struggle to generalize to previously unseen generative techniques. To overcome this limitation, we propose a novel detection framework based on Siamese Neural Networks (SNNs), which focus on learning the similarity between image pairs rather than relying on fixed class boundaries. The proposed architecture is coupled with an incremental training strategy, where generators that yield the lowest detection performance are progressively incorporated into the training process. This enables the model to adapt more effectively to a diverse and evolving range of synthetic content. Experimental results show that strong generalization can be achieved using training data from as few as two generators, demonstrating the efficiency and scalability of the proposed approach. However, it is also observed that increasing the number of generators beyond a certain point does not lead to further improvements in performance. This indicates potential limitations in the current SNN training process. 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In deepfake detection, the training labels typically indicate whether an image is real or fake, without specifying the semantic content, such as object classes. Moreover, we cannot know in advance the object categories present in an image to be detected. Ideally, a deepfake detection model should perform consistently across different semantic categories during inference, irrespective of the content. However, existing methods often exhibit significant performance bias between seen and unseen classes, struggling to generalize effectively. To address this issue, we propose Semantic-AGnostic artifact Network (SAGNet), an innovative and efficient approach designed to decouple semantic-agnostic artifacts from content-specific distributions in the training data. Our method eliminates semantic-specific biases, ensuring that the model focuses on universal artifacts related to image authenticity rather than content-dependent features. By employing this decoupling strategy, SAGNet greatly enhances the model’s generalization capacity, even when trained on limited data. Remarkably, through experiments, we demonstrate that SAGNet achieves performance comparable to models trained with 10 times more data, despite being trained on only 2 classes (comparing SAGNet trained on 2 classes with Ojha et al. (2023) trained on 20 categories). Furthermore, through extensive experiments, we show that SAGNet’s improvements are not only evident across different semantic categories but also extend to various generative methods, including multiple GAN-based and diffusion-based models. This cross-method generalization emphasizes SAGNet’s versatility and effectiveness in diverse generative scenarios. Overall, our method represents a significant advancement in deepfake detection, particularly in realistic situations where the training data is limited. 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This paper proposes a two-stage forensic evasion framework designed to undermine the detectability of diffusion-generated images. In the first stage, a spectrum-aware generative adversarial network (GAN) removes frequency-domain fingerprints that are commonly exploited by forensic models. In the second stage, adversarial perturbations are applied using the Iterative Fast Gradient Sign Method (I-FGSM) to further mislead detectors while preserving visual fidelity. Experiments conducted on COCO-based datasets demonstrate that our method significantly reduces detection accuracy across multiple state-of-the-art forensic models, including UniFD, DIGBD, and SSIP. Furthermore, we show that combining fingerprint removal with adversarial perturbation achieves stronger evasion than either method alone. Ablation studies also highlight the benefits of adaptive perturbation strengths and data augmentation for enhancing cross-model evasion. 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However, this progress also necessitates the development of effective detection methods, as synthetic images become increasingly difficult to distinguish from real ones. This difficulty leads to various societal issues, such as the spread of misinformation, identity theft, and online fraud. While previous detection methods perform well on public benchmarks, they struggle with our proposed benchmark, FakeART, particularly when dealing with the latest models and cross-domain tasks (e.g., photo-to-painting). To address this challenge, we develop a new synthetic image detection technique based on color distribution. Unlike real images, synthetic images often exhibit uneven color distribution. By employing color quantization and restoration techniques, we analyze the color differences before and after image restoration. We discover and prove that these differences closely relate to the uniformity of color distribution. 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Based on limited Convolutional Neural Network (CNN) capabilities in data descriptive processes, including long-range dependencies and global patterns, this study examines how Vision Transformer (ViT) can be utilized in detecting AI-generated interior design images. We finetuned and evaluated four ViT models, ViT-B16, ViT-B32, ViT-L16, and ViT-L32, on 1,000 samples per class dataset. Accuracy, precision, recall, F1-score, and computational efficiency were used to assess performance. Results show that models with smaller patch sizes (i.e., 16×16) perform better than larger ones (i.e., 32×32). It was found that ViT-B16 and ViT-L16 had the highest accuracy (96.25%) and F1- score (0.9625) in identifying minor inconsistencies in the AI-generated images. ViT-B32 and ViT-L32 enjoy better computational efficiency based on lower classification performance (80.00% and 81.25% accuracy, respectively, for ViT-B32 and ViT-L32). The best tradeoff between accuracy and resource efficiency turns out to be ViT-B16. However, computational costs were higher with ViT — ViT-L16, although just as accurate. Computationally, ViT-B32 and ViT-L32 were also efficient, which was more appropriate for realtime applications with lower accuracy than speed. Through this work, we contribute a domain-specific deep learning framework for AI-generated image detection in interior design to increase authenticity verification. Future work will address improving computational efficiency and generalizing the model across all (or most) generative models and design styles.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.31449/inf.v49i16.7979", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.31449/inf.v49i16.7979", "primary_url": "https://doi.org/10.31449/inf.v49i16.7979", "landing_page_url": "https://doi.org/10.31449/inf.v49i16.7979", "openalex_url": "https://openalex.org/W4409032835", "is_arxiv_preprint": false, "url": "https://doi.org/10.31449/inf.v49i16.7979", "authors": [ { "name": "Hui Wang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_locations": [ { "institution_name": "Anhui Business College", "institution_id": "institution:aa230e9d21cefa3b", "institution_type": "university", "country": "China", "country_code": "CN", "region": "", "region_code": "", "location_display": "China" } ], "aggregated_institutions": [ "Anhui Business College" ], "aggregated_institution_types": [ "university" ], "aggregated_country_names": [ "China" ], "aggregated_country_codes": [ "CN" ], "aggregated_regions": [], "aggregated_region_codes": [], "affiliation_review_state": "unreviewed", "institution_source": "automatic_fallback", "preliminary_affiliations": true, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "venue_id": "venue:informatica", "venue_acronym": "", "venue_track": "", "venue_label": "Informatica", "venue_aliases": [ "Informatica" ], "ambiguity_status": "unresolved", "raw_venue": "Informatica", "venue_review_required": true, "venue_review_reason": "Journal abbreviation has not been verified; confirm its established short name from the publisher or a bibliographic authority, or explicitly document that the full name is the standard short form.", "author_affiliation_counts": { "mapped": 1, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "affiliations": [ { "index": 1, "name": "Anhui Business College", "canonical_name": "Anhui Business College", "institution_id": "institution:aa230e9d21cefa3b", "institution_type": "university", "country": "China", "country_code": "", "region": "", "location_display": "China" } ], "current_institution": null, "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:aa230e9d21cefa3b", "institution": "Anhui Business College", "institution_type": "university", "authors": [ "Hui Wang" ], "mapping_source": "raw_affiliation", "mapping_fallback": false } ], "author_affiliation_indices": [ { "author": "Hui Wang", "indices": [ 1 ], "institution_ids": [ "institution:aa230e9d21cefa3b" ], "source": "raw_affiliation", "fallback": false } ], "author_institution_indices": [ { "author": "Hui Wang", "institution_indices": [ 1 ], "institution_ids": [ "institution:aa230e9d21cefa3b" ] } ], "formal_url": "https://doi.org/10.31449/inf.v49i16.7979", "metadata_status": { "overall": "Source metadata", "default_field_status": "Source metadata", "field_overrides": { "venue": { "status": "Needs review", "source": "OpenAlex" }, "affiliations": { "status": "Needs review" } }, "source": "OpenAlex" } }, { "paper_id": "curated:efceafb2be2246aa120b", "title": "Weakly‐Aligned Region‐Language Transformer for Real‐Time Artistic Content Detection in SAGIN", "in_scope": true, "year": 2025, "publication_year": 2025, "publication_date": "", "task": "detection", "venue": "Transactions on Emerging Telecommunications Technologies", "publisher": "", "publication_type": "journal", "abstract": "ABSTRACT The Space–Air–Ground Integrated Network (SAGIN) enables seamless connectivity across satellite, aerial, and terrestrial nodes. However, its heterogeneous architecture, resource‐constrained nodes, and dynamic link conditions pose significant challenges for deploying deep learning models‐particularly for detecting AI‐generated artistic content. These challenges include limited on‐board computational capacity, fluctuating bandwidth, and the requirement for fine‐grained visual‐semantic reasoning under weak supervision. To overcome these limitations, we propose the Weakly‐Aligned Region‐Language Transformer (WARL‐Transformer), a novel framework designed for robust AI‐generated content detection under realistic SAGIN constraints. WARL‐Transformer incorporates: (1) A vision‐language alignment mechanism that integrates local visual features with high‐level semantic cues derived from textual descriptions, and (2) a weakly supervised local feature alignment strategy that learns region‐language correspondences without relying on costly fine‐grained annotations. Laboratory‐based SAGIN emulation experiments further verify that WARL‐Transformer maintains high detection accuracy across diverse artistic styles while preserving robustness against network interference. In particular, WARL‐Transformer achieves an F1‐score of 96.78%, outperforming the baseline by +0.63 percentage points, and even under bandwidth‐constrained SAGIN emulation still reaches 99.7% of the full‐model F1, demonstrating strong robustness. This work establishes a foundation for reliable AI‐generated content detection in resource‐limited SAGIN settings, bridging the gap between visual content authentication and practical network‐driven constraints.", "abstract_source": "openalex", "ai_summary": "", "doi": "10.1002/ett.70335", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.1002/ett.70335", "primary_url": "https://doi.org/10.1002/ett.70335", "landing_page_url": "", "openalex_url": "https://openalex.org/W7117628628", "is_arxiv_preprint": false, "url": "https://doi.org/10.1002/ett.70335", "authors": [ { "name": "Jiayue Yu", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Sudip Kumar Sahana", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "openalex", "metadata_source": "openalex", "curation_status": "manually_confirmed", "review_status": "reviewed", "needs_review": false, "notes": "", "has_map_location": true, "map_record_count": 2, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_institutions": [ "Jilin Engineering Vocational College", "Birla Institute of Technology, Mesra (BIT)" ], "aggregated_country_names": [ "China", "India" ], "aggregated_country_codes": [ "CN", "IN" ], "aggregated_regions": [ "Jilin", "Jharkhand" ], "aggregated_region_codes": [], "curated_mappings": [ { "mapping_id": "mapping:14d2c27839905e0a4832", "institution_id": "institution:c45f90fe3d40e0f7", "location_id": "", "institution": "Jilin Engineering Vocational College", "institution_authors": [ "Jiayue Yu" ], "raw_affiliation": "Jilin Engineering Vocational College Siping China", "mapping_status": "active", "affiliation_order": 1 }, { "mapping_id": "mapping:3904f7c2221b4da84e8e", "institution_id": "institution:ffd387a3478eb9e9", "location_id": "", "institution": "Birla Institute of Technology, Mesra", "institution_authors": [ "Sudip Kumar Sahana" ], "raw_affiliation": "Department of Computer Science & Engineering Birla Institute of Technology Mesra India", "mapping_status": "active", "affiliation_order": 2 } ], "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:c45f90fe3d40e0f7", "institution": "Jilin Engineering Vocational College", "institution_type": "university", "authors": [ "Jiayue Yu" ], "mapping_source": "curated_admin", "mapping_fallback": false }, { "index": 2, "institution_id": "institution:ffd387a3478eb9e9", "institution": "Birla Institute of Technology, Mesra (BIT)", "institution_type": "university", "authors": [ "Sudip Kumar Sahana" ], "mapping_source": "curated_admin", "mapping_fallback": false, "source_institution_names": [ "Birla Institute of Technology, Mesra" ], "canonical_name": "Birla Institute of Technology, Mesra", "canonical_institution_name": "Birla Institute of Technology, Mesra", "abbreviation": "BIT" } ], "author_institution_indices": [ { "author": "Jiayue Yu", "institution_indices": [ 1 ], "institution_ids": [ "institution:c45f90fe3d40e0f7" ] }, { "author": "Sudip Kumar Sahana", "institution_indices": [ 2 ], "institution_ids": [ "institution:ffd387a3478eb9e9" ] } ], "author_affiliation_indices": [ { "author": "Jiayue Yu", "indices": [ 1 ], "institution_ids": [ "institution:c45f90fe3d40e0f7" ], "source": "curated_admin", "fallback": false }, { "author": "Sudip Kumar Sahana", "indices": [ 2 ], "institution_ids": [ "institution:ffd387a3478eb9e9" ], "source": "curated_admin", "fallback": false } ], "affiliation_review_state": "curated", "institution_source": "curated", "preliminary_affiliations": false, "resolution_notes": "Preliminary automatic affiliation evidence; 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Recovery-Based Black-Box Detection of AI-Generated Content", "in_scope": true, "year": 2025, "publication_year": 2025, "publication_date": "", "task": "detection", "venue": "IEEE/CVF Conference on Computer Vision and Pattern Recognition", "venue_name": "IEEE/CVF Conference on Computer Vision and Pattern Recognition", "venue_type": "conference", "publisher": "", "publication_type": "conference", "abstract": "The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real ones to the human eye. While offering new creative and commercial possibilities, the potential for misuse, such as in misinformation and fraud, highlights the need for effective detection methods. Current detection approaches often rely on access to model weights or require extensive collections of real image datasets, limiting their scalability and practical application in real world scenarios. In this work, we introduce a novel black box detection framework that requires only API access, sidestepping the need for model weights or large auxiliary datasets. Our approach leverages a corrupt and recover strategy: by masking part of an image and assessing the model ability to reconstruct it, we measure the likelihood that the image was generated by the model itself. For black-box models that do not support masked image inputs, we incorporate a cost efficient surrogate model trained to align with the target model distribution, enhancing detection capability. Our framework demonstrates strong performance, outperforming baseline methods by 4.31% in mean average precision across eight diffusion model variant datasets.", "abstract_source": "openalex", "ai_summary": "", "doi": "10.1109/cvpr52734.2025.02684", "arxiv_id": "2505.01008", "arxiv_url": "https://arxiv.org/abs/2505.01008", "arxiv_year": null, "has_arxiv_version": true, "paper_url": "https://doi.org/10.1109/cvpr52734.2025.02684", "primary_url": "https://doi.org/10.1109/cvpr52734.2025.02684", "landing_page_url": "", "openalex_url": "https://openalex.org/W4414769856", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/cvpr52734.2025.02684", "authors": [ { "name": "Haoyue Bai", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Yiyou Sun", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", 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The complexity and the sheer number of available generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for this task, make training and benchmarking synthetic image source attribution models very challenging. WILD1 is a new in-the-Wild Image Linkage Dataset designed to provide a powerful training and benchmarking tool for synthetic image attribution models. The dataset is built out of a closed set of 10 popular commercial generators, which constitutes the training base of attribution models, and an open set of 10 additional generators, simulating a real-world in-the-wild scenario. Each generator is represented by 1,000 images, for a total of 10,000 images in the closed set and 10,000 images in the open set. Half of the images are post-processed with a wide range of operators. WILD allows benchmarking attribution models in a wide range of tasks, including closed and open set identification and verification, and robust attribution with respect to post-processing and adversarial attacks. Models trained on WILD are expected to benefit from the challenging scenario represented by the dataset itself. 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In this paper, we propose a verification framework that relies on a Siamese Network to address the problem of open-set attribution of synthetic images to the architecture that generated them. We consider two different settings. In the first setting, the system determines whether two images have been produced by the same generative architecture or not. In the second setting, the system verifies a claim about the architecture used to generate a synthetic image, utilizing one or multiple reference images generated by the claimed architecture. The main strength of the proposed system is its ability to operate in both closed and open-set scenarios so that the input images, either the query and reference images, can belong to the architectures considered during training or not. 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Renowned for their ability to recognize local features, share weights, and employ pooling mechanisms, CNNs form the foundation of this innovative approach. The study presents a novel method that harnesses CNNs' inherent strengths to elevate image detection. Central to this method is the incorporation of a specialized module called \"ShortCut3- ResNet,\" inspired by the Residual Network (ResNet) concept. This module enhances the network's capacity to capture intricate image details, thereby facilitating more precise feature extraction. An integral facet of the proposed technique is the establishment of a dual optimization model. By harmonizing the convolution and full connection processes within the CNN, this model amplifies the network's ability to understand intricate image patterns. By systematically exploring a spectrum of CNN parameter configurations, the optimal setting is identified. This approach markedly enhances the CNN's proficiency in extracting pertinent features from images, leading to substantial improvements in its image recognition accuracy. In summary, this abstract underscore a pioneering advancement in image detection, leveraging the prowess of CNNs. By integrating a specialized module and refining the learning process, this technique augments the CNN's capability to discern complex image patterns. This innovation holds transformative potential, spanning applications that range from refining image recognition systems to enhancing the precision of AI-generated image detection. Through this advancement, the intersection of CNNs and image detection propels the field towards new horizons of accuracy and efficacy. Keyword: Image detection, Convolutional Neural Networks (CNNs), Local features, Share weights, Pooling mechanisms, ShortCut3-ResNet, Residual Network (ResNet), Feature extraction.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.55041/ijsrem29037", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.55041/ijsrem29037", "primary_url": "https://doi.org/10.55041/ijsrem29037", "landing_page_url": "https://doi.org/10.55041/ijsrem29037", "openalex_url": "https://openalex.org/W4392457694", "is_arxiv_preprint": false, "url": "https://doi.org/10.55041/ijsrem29037", "authors": [ { "name": "Mrs D. 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This technology aims to detect whether an image is naturally generated by imaging systems (e.g., digital cameras) or generated by advanced AI techniques. Despite the promising performance achieved by recent fake detection methods, they are typically trained on millions of redundant images with similar characteristics, leading to inefficient training. Furthermore, the performances of existing detectors often deteriorate when the training datasets are imbalanced. To address these challenges, we propose a novel AI-generated image detector based on dynamic aggregation and information compression with the Wasserstein distance. Experimental results show that our proposed method significantly outperforms state-of-the-art models that generalize across different generative models, with an increase of $\\mathbf{+ 1. 8 6 \\%}$ average accuracy and $\\mathbf{+ 0. 1 4 \\%}$ average precision, while substantially reducing the training time. On imbalanced datasets, our proposed method leads to a $\\mathbf{+ 1 4. 4 6 \\%}$ accuracy improvement, clearly demonstrating its robustness on imbalanced datasets.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/icip51287.2024.10648186", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.1109/icip51287.2024.10648186", "primary_url": "https://doi.org/10.1109/icip51287.2024.10648186", "landing_page_url": "https://doi.org/10.1109/icip51287.2024.10648186", "openalex_url": "https://openalex.org/W4402917177", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/icip51287.2024.10648186", "authors": [ { "name": "Zihang Lyu", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Jun Xiao", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Cong Zhang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Kin‐Man Lam", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_locations": [ { "institution_name": "The Hong Kong Polytechnic University", "institution_id": "institution:31f980eaa403801a", "institution_type": "university", "country": "China", "country_code": "CN", "region": "Hong Kong", "region_code": "HK", "location_display": "Hong Kong, China" } ], "aggregated_institutions": [ "The Hong Kong Polytechnic University" ], "aggregated_institution_types": [ "university" ], "aggregated_country_names": [ "China" ], "aggregated_country_codes": [ "CN" ], "aggregated_regions": [ "Hong Kong" ], "aggregated_region_codes": [ "HK" ], "affiliation_review_state": "unreviewed", "institution_source": "automatic_fallback", "preliminary_affiliations": true, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "venue_id": "venue:ieee-international-conference-on-image-processing", "venue_acronym": "ICIP", "venue_track": "Main", "venue_label": "IEEE International Conference on Image Processing (ICIP)", "venue_aliases": [ "IEEE international conference on image processing", "IEEE International Conference on Image Processing (ICIP)" ], "ambiguity_status": "resolved", "raw_venue": "2024 IEEE International Conference on Image Processing (ICIP)", "venue_review_required": false, "author_affiliation_counts": { "mapped": 4, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "affiliations": [ { "index": 1, "name": "The Hong Kong Polytechnic University", "canonical_name": "The Hong Kong Polytechnic University", "institution_id": "institution:31f980eaa403801a", "institution_type": "university", "country": "China", "country_code": "CN", "region": "Hong Kong", "location_display": "Hong Kong, China", "source_institution_names": [ "Hong Kong Polytechnic University" ] } ], "current_institution": null, "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:31f980eaa403801a", "institution": "The Hong Kong Polytechnic University", "institution_type": "university", "authors": [ "Zihang Lyu", "Jun Xiao", "Cong Zhang", "Kin‐Man Lam" ], "mapping_source": "raw_affiliation", "mapping_fallback": false, "source_institution_names": [ "Hong Kong Polytechnic University" ] } ], "author_affiliation_indices": [ { "author": "Zihang Lyu", "indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ], "source": "raw_affiliation", "fallback": false }, { "author": "Jun Xiao", "indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ], "source": "raw_affiliation", "fallback": false }, { "author": "Cong Zhang", "indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ], "source": "raw_affiliation", "fallback": false }, { "author": "Kin‐Man Lam", "indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ], "source": "raw_affiliation", "fallback": false } ], "author_institution_indices": [ { "author": "Zihang Lyu", "institution_indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ] }, { "author": "Jun Xiao", "institution_indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ] }, { "author": "Cong Zhang", "institution_indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ] }, { "author": "Kin‐Man Lam", "institution_indices": [ 1 ], "institution_ids": [ "institution:31f980eaa403801a" ] } ], "formal_url": "https://doi.org/10.1109/icip51287.2024.10648186", "metadata_status": { "overall": "Source metadata", "default_field_status": "Source metadata", "field_overrides": { "affiliations": { "status": "Needs review" } }, "source": "OpenAlex" } }, { "title": "AI-generated Image Detection: Passive or Watermark?", "in_scope": true, "year": 2024, "publication_year": 2024, "publication_date": "2024-11-20", "task": "detection", "paper_categories": [ "method" ], "venue": "arXiv", "venue_name": "arXiv", "venue_type": "preprint", "publisher": "Cornell University", "publication_type": "preprint", "abstract": "While text-to-image models offer numerous benefits, they also pose significant societal risks. Detecting AI-generated images is crucial for mitigating these risks. Detection methods can be broadly categorized into passive and watermark-based approaches: passive detectors rely on artifacts present in AI-generated images, whereas watermark-based detectors proactively embed watermarks into such images. A key question is which type of detector performs better in terms of effectiveness, robustness, and efficiency. However, the current literature lacks a comprehensive understanding of this issue. In this work, we aim to bridge that gap by developing ImageDetectBench, the first comprehensive benchmark to compare the effectiveness, robustness, and efficiency of passive and watermark-based detectors. Our benchmark includes four datasets, each containing a mix of AI-generated and non-AI-generated images. We evaluate five passive detectors and four watermark-based detectors against eight types of common perturbations and three types of adversarial perturbations. Our benchmark results reveal several interesting findings. For instance, watermark-based detectors consistently outperform passive detectors, both in the presence and absence of perturbations. Based on these insights, we provide recommendations for detecting AI-generated images, e.g., when both types of detectors are applicable, watermark-based detectors should be the preferred choice. Our code and data are publicly available at https://github.com/moyangkuo/ImageDetectBench.git.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.48550/arxiv.2411.13553", "arxiv_id": "2411.13553", "arxiv_url": "https://arxiv.org/abs/2411.13553", "arxiv_year": 2024, "has_arxiv_version": true, "paper_url": "", "primary_url": "https://openalex.org/W4404649743", "landing_page_url": "http://arxiv.org/abs/2411.13553", "openalex_url": "https://openalex.org/W4404649743", "is_arxiv_preprint": true, "url": "https://openalex.org/W4404649743", "authors": [ { "name": "Moyang Guo", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Yuepeng Hu", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Zhengyuan Jiang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Zeyu Li", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Amir Sadovnik", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Arka Daw", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Neil Zhenqiang Gong", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; 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This has triggered several research efforts to accurately detect deepfake images, achieving excellent performance on publicly available deepfake datasets. In this work, we study 8 state-of-the-art detectors and argue that they are far from being ready for deployment due to two recent developments. First, the emergence of lightweight methods to customize large generative models, can enable an attacker to create many customized generators (to create deepfakes), thereby substantially increasing the threat surface. We show that existing defenses fail to generalize well to such user-customized generative models that are publicly available today. We discuss new machine learning approaches based on content-agnostic features, and ensemble modeling to improve generalization performance against user-customized models. Second, the emergence of vision foundation models—machine learning models trained on broad data that can be easily adapted to several downstream tasks—can be misused by attackers to craft adversarial deepfakes that can evade existing defenses. We propose a simple adversarial attack that leverages existing foundation models to craft adversarial samples without adding any adversarial noise, through careful semantic manipulation of the image content. We highlight the vulnerabilities of several defenses against our attack, and explore directions leveraging advanced foundation models and adversarial training to defend against this new threat.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/sp54263.2024.00194", "arxiv_id": "2404.16212", "arxiv_url": "https://arxiv.org/abs/2404.16212", "arxiv_year": 2024, "has_arxiv_version": true, "paper_url": "https://doi.org/10.1109/sp54263.2024.00194", "primary_url": "https://doi.org/10.1109/sp54263.2024.00194", "landing_page_url": "https://doi.org/10.1109/sp54263.2024.00194", "openalex_url": "https://openalex.org/W4402264364", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/sp54263.2024.00194", "authors": [ { "name": "Sifat Muhammad Abdullah", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Aravind Cheruvu", 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Detecting deepfake images has become a critical research area, with numerous techniques developed using deep learning methods. This study presents a strategy for spotting deepfake images with a model that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The combination blends CNNs' spatial awareness with LSTMs' grasp of temporal context. Through successful performance on accessible datasets, the developed approach achieves an accuracy rate of 98.20%, showcasing its ability to accurately discern deepfake images while maintaining a low false-positive output. With an error of 0.28%, the model highlights the complexities and challenges in deepfake detection. 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The deepfake images were generated using FaceApp, a prominent tool for creating manipulated visuals. Our findings demonstrate that VGG19 outperforms both VGG16 and ResNet50, achieving an impressive accuracy rate of 98% on the test dataset when small size is small. By harnessing the power of AI, particularly through the application of CNN algorithms, this research makes a significant contribution to the field of deepfake image detection. 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To overcome this new challenge, Developing an effective method in the context of detection of deep images. In this study, we conduct a comparative analysis of three varied convolutional neural networks (CNNs) for deepfake image detection. Our experimental results highlight the strengths and weaknesses of each CNN architecture. We deliberate on the consequences of our results in the context of deep image perception and show which models may be better for certain situations. We also address the challenges and limitations associated with deep learning, such as the arms race of deep learning technologies and tools. In conclusion, our work adds to the expanding body of knowledge regarding deep image detection by comparing three major CNN architectures. Our findings provide important guidance for researchers, prac- titioners, and policymakers working to improve the security and authenticity of content in an increasingly digital age. 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Keywords—Deepfake, image detection, convolutional neural networks (CNNs), ResNet, InceptionV3, DenseNet, face forgery detection, GAN, forensics, deep learning, artificial intelligence, convolutional layers, pooling layers, fully connected layers, augmented data, accuracy, precision, recall, F1 measure, Area Under Receiver Operating Characteristic Curve.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.55041/ijsrem30215", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.55041/ijsrem30215", "primary_url": "https://doi.org/10.55041/ijsrem30215", "landing_page_url": "http://dx.doi.org/10.55041/ijsrem30215", "openalex_url": "https://openalex.org/W4394009764", "is_arxiv_preprint": false, "url": "https://doi.org/10.55041/ijsrem30215", "authors": [ { "name": "Mrs. Prajwal S", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_locations": [ { "institution_name": "AISSMS Institute of Information Technology", "institution_id": "institution:f31d282d4be83051", "institution_type": "other", "country": "India", "country_code": "", "region": "", "region_code": "", "location_display": "India" } ], "aggregated_institutions": [ "AISSMS Institute of Information Technology" ], "aggregated_institution_types": [ "other" ], "aggregated_country_names": [ "India" ], "aggregated_country_codes": [], "aggregated_regions": [], "aggregated_region_codes": [], "affiliation_review_state": "unreviewed", "institution_source": "automatic_fallback", "preliminary_affiliations": true, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "venue_id": "venue:international-journal-of-scientific-research-in-engineering-and-management", "venue_acronym": "IJSREM", "venue_track": "", "venue_label": "International Journal of Scientific Research in Engineering and Management (IJSREM)", "venue_aliases": [ "International Journal of Scientific Research in Engineering and Management", "INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT", "IJSREM" ], "ambiguity_status": "resolved", "raw_venue": "INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT", "venue_review_required": false, "author_affiliation_counts": { "mapped": 1, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "affiliations": [ { "index": 1, "name": "AISSMS Institute of Information Technology", "canonical_name": "AISSMS Institute of Information Technology", "institution_id": "institution:f31d282d4be83051", "institution_type": "other", "country": "India", "country_code": "", "region": "", "location_display": "India" } ], "current_institution": null, "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:f31d282d4be83051", "institution": "AISSMS Institute of Information Technology", "institution_type": "other", "authors": [ "Mrs. Prajwal S" ], "mapping_source": "raw_affiliation", "mapping_fallback": false } ], "author_affiliation_indices": [ { "author": "Mrs. Prajwal S", "indices": [ 1 ], "institution_ids": [ "institution:f31d282d4be83051" ], "source": "raw_affiliation", "fallback": false } ], "author_institution_indices": [ { "author": "Mrs. Prajwal S", "institution_indices": [ 1 ], "institution_ids": [ "institution:f31d282d4be83051" ] } ], "formal_url": "https://doi.org/10.55041/ijsrem30215", "metadata_status": { "overall": "Source metadata", "default_field_status": "Source metadata", "field_overrides": { "affiliations": { "status": "Needs review" } }, "source": "OpenAlex" } }, { "title": "Deepfake Image Detection Using ResNet50 Model", "in_scope": true, "year": 2024, "publication_year": 2024, "publication_date": "2024-11-06", "task": "detection", "paper_categories": [ "method" ], "venue": "International Conference on Cyber Security and Computing", "venue_name": "International Conference on Cyber Security and Computing", "venue_type": "conference", "publisher": "", "publication_type": "conference", "abstract": "Deepfake images, created through advanced AI techniques, pose significant cybersecurity risks, facilitating identity fraud and potentially damaging reputations and financial stability. The primary challenge in detecting deepfakes lies in ensuring algorithms generalize well across diverse datasets, minimizing errors like False Positives (FP) and False Negatives (FN) on unseen data. This study focuses on enhancing deepfake detection using the ResNet50 model and Gaussian blur preprocessing to bolster model generalizability. The methodology encompasses five phases: Data Collection, Data Splitting, Data Pre-processing, Training, and Model Testing on the DeepFakeFace and Deepfake Processed datasets. Evaluations, including accuracy, specificity, recall, and precision, are conducted on 4000 images per dataset split in ratios of 60:20:20 and 80:10:10. Results indicate superior performance with the 80:10:10 split, achieving peak accuracy of 82.75%. Gaussian blur notably enhances specificity, increasing from 54.67% to 65.00% with the 80:10:10 ratio, underscoring its role in improving model robustness across varied datasets. This research highlights critical advancements in cybersecurity measures against deepfake threats.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/cybercomp60759.2024.10913843", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.1109/cybercomp60759.2024.10913843", "primary_url": "https://doi.org/10.1109/cybercomp60759.2024.10913843", "landing_page_url": "https://doi.org/10.1109/cybercomp60759.2024.10913843", "openalex_url": "https://openalex.org/W4408401076", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/cybercomp60759.2024.10913843", "authors": [ { "name": "Lew Kar Yee", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Isredza Rahmi A. 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There are various categories of deepfake images as the technology evolves and becomes more accessible. In parallel, deepfake detection methods are also improving, from basic features analysis to pairwise analysis and deep learning; nevertheless, to date, there is no consistent method able to fully detect such images. This study aims to provide an overview of existing methods of deepfake detection in the literature and investigate the accuracy of models based on Vision Transformer (VIT) when analysing and detecting deepfake images. We implement a VIT model-based deepfake detection technique, which is trained and tasted on a mixed real and deepfake images dataset from Kaggle, containing 40000 images. The results show that The VIT model scores relatively high, 89.9125 %, which demonstrates its potential but also highlights there is significant room for improvement. Preliminary tests also highlight the importance of a large dataset for training and the fast convergence of the model. 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In order to evaluate the efficacy of widely recognized deep learning models in detecting and classifying deepfake images, we compare and contrast YOLO (You Only Look Once) V8, CNN (Convolutional Neural Network), combination of LSTM (Long Short-Term Memory) with CNN and ResNet in this paper. The capacity to recognize photos that have been manipulated effectively is essential for maintaining trust regarding digital media and preventing the spread of deceptive data. The objective of our research is to explain each model's abilities, limitations and performance in the context of deepfake image recognition. We evaluated each model's accuracy, precision, and F1-scores using a dataset of 190402 images, half real and half fake that cover a wide spectrum of deep fake images. Based on the evaluation metrics, each model was ranked in terms of its ability to separately describe and determine original versus generated photos with focus on detecting subtle changes eluding human senses. The comparison research shows each model's nuanced capabilities, offering light on the implications for applications in the real world like detecting and controlling the spread of deep fake information across numerous internet platforms. Our findings help towards building deep fake detection systems, further understanding the comparative performance of state of the art deep architectures. This study shall inform more effective deep fake detection systems that can guarantee trust and security in digital media environments. 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Detecting and classifying deepfake images with high accuracy is crucial to addressing this growing challenge. Deepfake images, generated using advanced machine learning techniques, have become a significant concern due to their potential to deceive individuals and manipulate information. This paper presents DeepSight, an innovative approach that combines ensemble learning techniques with deep learning models to enhance deepfake image detection and classification. DeepSight leverages the diversity of multiple classifiers trained on distinct feature representations extracted from the input image data. The framework begins with an initial assessment to determine whether the image has been manipulated. Subsequently, the image goes through deep feature extraction using Convolutional Neural Networks (CNNs). The resulting feature vectors are then classified using Random Forest, KNearest Neighbors, and XGBoost, with hyper-parameter optimization. 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Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). 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The necessity to distinguish real from AI-generated content is becoming increasingly critical to preserve the integrity of online data. While traditional methods for detecting fake images resulting from image tampering rely on hand-crafted features, the sophistication of manipulated images produced by generative adversarial networks requires more advanced detection approaches. The lightweight approach proposed here is based on convolutional neural networks that comprise only eight convolutional and two hidden layers that effectively differentiate AI-generated images from real ones. The proposed approach was assessed using two benchmark datasets and custom-generated data from Sentinel-2 imagery. It demonstrated superior performance compared to four state-of-the-art methods on the CIFAKE dataset, achieving the highest accuracy of 97.32%, on par with the highest-performing state-of-the-art method. Explainable AI is utilized to enhance our comprehension of the complex processes involved in synthetic image recognition. 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Spectra anomalies are produced by up-sampling processes, standard procedures in GAN systems for making large amounts of fake data films. The Compact Ensemble discriminator (CED) concentrates on the most distinguishing feature between the natural and synthetic images, giving the generators a robust training signal. As empirical results on publicly available datasets show, the suggested algorithms outperform state-of-the-art methods and the proposed CED-DCGAN technique successfully detects high-fidelity deep fakes in video conferencing and generalizes well when comparing with other techniques. 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Unlike methods that alter portions of an image, the creation of completely synthetic images presents a unique challenge and several Synthetic Image Detection (SID) methods have recently appeared to tackle it. Yet, there is often a large gap between experimental results on benchmark datasets and the performance of methods in the wild. To better address the evaluation needs of SID and help close this gap, this paper introduces a benchmarking framework that integrates several state-of-the-art SID models. Our selection of integrated models was based on the utilization of varied input features, and different network architectures, aiming to encompass a broad spectrum of techniques. The framework leverages recent datasets with a diverse set of generative models, high level of photo-realism and resolution, reflecting the rapid improvements in image synthesis technology. Additionally, the framework enables the study of how image transformations, common in assets shared online, such as JPEG compression, affect detection performance. 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Addressing this issue, tracing back to the source model of generated images has become a crucial approach to resolving it, aiding in establishing the legitimacy and ownership of images. Most existing approaches primarily focus on discerning image authenticity while overlooking the issue of source attribution for generated images. Few traditional image attribution methods are constrained by temporal limitations, only applicable to attributing past generative models such as GANs, while their effectiveness is limited for newer methods like diffusion generative models that have emerged in recent years. This paper proposes a novel method capable of attributing images generated by text-to-image diffusion models and maintaining effectiveness even for untrained models. We construct a semantic feature extraction network and train a feature-difference recognition model using a siamese network approach based on the semantic similarity of generated images. 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This task is crucial for understanding the vulnerabilities of existing detection methods and developing more robust techniques. However, current adversarial attacks often introduce visible noise, have poor transferability, and fail to address spectral differences between AI-generated and genuine images. To address this, we propose StealthDiffusion, a framework based on stable diffusion that modifies AI-generated images into high-quality, imperceptible adversarial examples capable of evading state-of-the-art forensic detectors. StealthDiffusion comprises two main components: Latent Adversarial Optimization, which generates adversarial perturbations in the latent space of stable diffusion, and Control-VAE, a module that reduces spectral differences between the generated adversarial images and genuine images without affecting the original diffusion model's generation process. Extensive experiments show that StealthDiffusion is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries with frequency spectra similar to genuine images. 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However, owing to the disparities between CG and AIGC, conventional CG detection methods tend to be inadequate in identifying AIGC-produced images. To address this issue, our research concentrates on the text-to-image generation process in AIGC. Initially, we first assemble two text-to-image databases utilizing two distinct AI systems, DALL•E2 and DreamStudio. Aiming to holistically capture the inherent anomalies produced by AIGC, we develope a robust dual-stream network comprised of a residual stream and a content stream. The former employs the Spatial Rich Model (SRM) to meticulously extract various texture information from images, while the latter seeks to capture additional forged traces in low frequency, thereby extracting complementary information that the residual stream may overlook. To enhance the information exchange between these two streams, we incorporate a cross multi-head attention mechanism. Numerous comparative experiments are performed on both databases, and the results show that our detection method consistently outperforms traditional CG detection techniques across a range of image resolutions. Moreover, our method exhibits superior performance through a series of robustness tests and cross-database experiments. 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A generative adversarial network (GAN) can fake the captured media streams, such as images, audio, and video, and make them visually fit other environments. So, the dissemination of fake media streams creates havoc in social communities and can destroy the reputation of a person or a community. Moreover, it manipulates public sentiments and opinions toward the person or community. Recent studies have suggested using the convolutional neural network (CNN) as an effective tool to detect deepfakes in the network. But, most techniques cannot capture the inter-frame dissimilarities of the collected media streams. Motivated by this, this paper presents a novel and improved deep-CNN (D-CNN) architecture for deepfake detection with reasonable accuracy and high generalizability. Images from multiple sources are captured to train the model, improving overall generalizability capabilities. The images are re-scaled and fed to the D-CNN model. 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However, this increase in the use of digital images has also led to a rise in the use of techniques to alter image contents. Therefore, it is essential for both the image forensics field and the general public to be able to differentiate between genuine or authentic images and manipulated or fake imagery. Deep learning has made it easier to create unreal images, which underscores the need to establish a more robust platform to detect real from fake imagery. However, in the image forensics field, researchers often develop very complicated deep learning architectures to train the model. This training process is expensive, and the model size is often huge, which limits the usability of the model. This research focuses on the realism of state-of-the-art image manipulations and how difficult it is to detect them automatically or by humans. We built a machine learning model called G-JOB GAN, based on Generative Adversarial Networks (GAN), that can generate state-of-the-art, realistic-looking images with improved resolution and quality. Our model can detect a realistically generated image with an accuracy of 95.7%. Our near future aim is to implement a system that can detect fake images with a probability of odds of 1- P, where P is the chance of identical fingerprints. To achieve this objective, we have implemented and evaluated various GAN architectures such as Style GAN, Pro GAN, and the Original GAN.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/cacre58689.2023.10208613", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.1109/cacre58689.2023.10208613", "primary_url": "https://doi.org/10.1109/cacre58689.2023.10208613", "landing_page_url": "https://doi.org/10.1109/cacre58689.2023.10208613", "openalex_url": "https://openalex.org/W4385656076", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/cacre58689.2023.10208613", "authors": [ { "name": "Galamo Monkam", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Jie Yan", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_locations": [ { "institution_name": "Bowie State University", "institution_id": "institution:86acee6f855e6b06", "institution_type": "university", "country": "United States", "country_code": "US", "region": "", "region_code": "", "location_display": "United States" } ], "aggregated_institutions": [ "Bowie State University" ], "aggregated_institution_types": [ "university" ], "aggregated_country_names": [ "United States" ], "aggregated_country_codes": [ "US" ], "aggregated_regions": [], "aggregated_region_codes": [], "affiliation_review_state": "unreviewed", "institution_source": "automatic_fallback", "preliminary_affiliations": true, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "venue_id": "venue:international-conference-on-automation-control-and-robotics-engineering", "venue_acronym": "CACRE", "venue_track": "Main", "venue_label": "International Conference on Automation, Control and Robotics Engineering (CACRE)", "venue_aliases": [ "International Conference on Automation, Control and Robotics Engineering", "2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE)", "CACRE" ], "ambiguity_status": "resolved", "raw_venue": "2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE)", "venue_review_required": false, "author_affiliation_counts": { "mapped": 2, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "affiliations": [ { "index": 1, "name": "Bowie State University", "canonical_name": "Bowie State University", "institution_id": "institution:86acee6f855e6b06", "institution_type": "university", "country": "United States", "country_code": "", "region": "", "location_display": "United States" } ], "current_institution": null, "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:86acee6f855e6b06", "institution": "Bowie State University", "institution_type": "university", "authors": [ "Galamo Monkam", "Jie Yan" ], "mapping_source": "raw_affiliation", "mapping_fallback": false } ], "author_affiliation_indices": [ { "author": "Galamo Monkam", "indices": [ 1 ], "institution_ids": [ "institution:86acee6f855e6b06" ], "source": "raw_affiliation", "fallback": false }, { "author": "Jie Yan", "indices": [ 1 ], "institution_ids": [ "institution:86acee6f855e6b06" ], "source": "raw_affiliation", "fallback": false } ], "author_institution_indices": [ { "author": "Galamo Monkam", "institution_indices": [ 1 ], "institution_ids": [ "institution:86acee6f855e6b06" ] }, { "author": "Jie Yan", "institution_indices": [ 1 ], "institution_ids": [ "institution:86acee6f855e6b06" ] } ], "formal_url": "https://doi.org/10.1109/cacre58689.2023.10208613", "metadata_status": { "overall": "Source metadata", "default_field_status": "Source metadata", "field_overrides": { "affiliations": { "status": "Needs review" } }, "source": "OpenAlex" } }, { "title": "DIRE for Diffusion-Generated Image Detection", "in_scope": true, "year": 2023, "publication_year": 2023, "publication_date": "2023-10-01", "task": "detection", "paper_categories": [ "method", "dataset" ], "venue": "IEEE/CVF International Conference on Computer Vision", "venue_name": "IEEE/CVF International Conference on Computer Vision", "venue_type": "conference", "publisher": "", "publication_type": "conference", "abstract": "Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images. We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data. To address this issue, we propose a novel image representation called DIffusion Reconstruction Error (DIRE), which measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. We observe that diffusion-generated images can be approximately reconstructed by a diffusion model while real images cannot. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images. DIRE provides an effective way to detect images generated by most diffusion models, and it is general for detecting generated images from unseen diffusion models and robust to various perturbations. Furthermore, we establish a comprehensive diffusion-generated benchmark including images generated by various diffusion models to evaluate the performance of diffusion-generated image detectors. Extensive experiments on our collected benchmark demonstrate that DIRE exhibits superiority over previous generated-image detectors. The code, models, and dataset are available at https://github.com/ZhendongWang6/DIRE.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/iccv51070.2023.02051", "arxiv_id": "2303.09295", "arxiv_url": "https://arxiv.org/abs/2303.09295", "arxiv_year": 2023, "has_arxiv_version": true, "paper_url": "https://doi.org/10.1109/iccv51070.2023.02051", "primary_url": "https://doi.org/10.1109/iccv51070.2023.02051", "landing_page_url": "https://doi.org/10.1109/iccv51070.2023.02051", "openalex_url": "https://openalex.org/W4390873235", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/iccv51070.2023.02051", "authors": [ { "name": "Zhendong Wang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Jianmin Bao", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Wengang Zhou", "affiliation_indices": [ 1, 3 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Weilun Wang", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Hezhen Hu", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Hong Chen", "affiliation_indices": [ 4 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Houqiang Li", "affiliation_indices": [ 1, 3 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "openalex", "needs_review": false, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review.", "has_map_location": true, "map_record_count": 4, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_locations": [ { "institution_name": "University of Science and Technology of China", "institution_id": "institution:e721b03b6f6c172d", "institution_type": "university", "country": "China", "country_code": "CN", "region": "Anhui", "region_code": "", "location_display": "Anhui, China" }, { "institution_name": "Microsoft Research Asia", "institution_id": "institution:7b971ad17fd639eb", "institution_type": "company", "country": "China", "country_code": "CN", "region": "Beijing", "region_code": "", "location_display": "Beijing, China" }, { "institution_name": "Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "institution_id": "institution:db049bef3a2d25e0", "institution_type": "research_unit", "country": "China", "country_code": "CN", "region": "Anhui", "region_code": "", "location_display": "Anhui, China" }, { "institution_name": "Merchants Union Consumer Finance Company", "institution_id": "institution:73df77aca515d21a", "institution_type": "company", "country": "China", "country_code": "CN", "region": "Guangdong", "region_code": "", "location_display": "Guangdong, China" } ], "aggregated_institutions": [ "University of Science and Technology of China", "Microsoft Research Asia", "Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "Merchants Union Consumer Finance Company" ], "aggregated_institution_types": [ "university", "company", "research_unit" ], "aggregated_country_names": [ "China" ], "aggregated_country_codes": [ "CN" ], "aggregated_regions": [ "Anhui", "Beijing", "Guangdong" ], "aggregated_region_codes": [], "paper_id": "curated:5d3c6fda5b268e0153ec", "venue_id": "venue:iccv", "venue_acronym": "ICCV", "venue_track": "Main", "raw_venue": "2023 IEEE/CVF International Conference on Computer Vision (ICCV)", "metadata_source": "openalex", "curation_status": "confirmed", "review_status": "reviewed", "formal_url": "https://doi.org/10.1109/iccv51070.2023.02051", "affiliation_review_state": "curated", "curated_mappings": [ { "mapping_id": "mapping:eabf85fb6bbe00464322", "institution_id": "institution:e721b03b6f6c172d", "location_id": "", "institution": "University of Science and Technology of China", "institution_authors": [ "Zhendong Wang", "Wengang Zhou", "Weilun Wang", "Hezhen Hu", "Houqiang Li" ], "raw_affiliation": "CAS Key Laboratory of GIPAS, EEIS Department, University of Science and Technology of China", "mapping_status": "active", "affiliation_order": 1 }, { "mapping_id": "mapping:be6f5040098cef042c7c", "institution_id": "institution:7b971ad17fd639eb", "location_id": "", "institution": "Microsoft 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"institution:e721b03b6f6c172d", "institution": "University of Science and Technology of China", "institution_type": "university", "authors": [ "Zhendong Wang", "Wengang Zhou", "Weilun Wang", "Hezhen Hu", "Houqiang Li" ], "mapping_source": "curated_admin", "mapping_fallback": false }, { "index": 2, "institution_id": "institution:7b971ad17fd639eb", "institution": "Microsoft Research Asia", "institution_type": "company", "authors": [ "Jianmin Bao" ], "mapping_source": "curated_admin", "mapping_fallback": false }, { "index": 3, "institution_id": "institution:db049bef3a2d25e0", "institution": "Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "institution_type": "research_unit", "authors": [ "Wengang Zhou", "Houqiang Li" ], "mapping_source": "curated_admin", "mapping_fallback": false }, { "index": 4, "institution_id": "institution:73df77aca515d21a", "institution": "Merchants Union Consumer Finance Company", "institution_type": "company", "authors": [ "Hong 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"institution_id": "institution:7b971ad17fd639eb", "institution_type": "company", "country": "China", "country_code": "", "region": "Beijing", "location_display": "Beijing, China" }, { "index": 3, "name": "Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "canonical_name": "Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "institution_id": "institution:db049bef3a2d25e0", "institution_type": "research_unit", "country": "China", "country_code": "", "region": "Anhui", "location_display": "Anhui, China" }, { "index": 4, "name": "Merchants Union Consumer Finance Company", "canonical_name": "Merchants Union Consumer Finance Company", "institution_id": "institution:73df77aca515d21a", "institution_type": "company", "country": "China", "country_code": "", "region": "Guangdong", "location_display": "Guangdong, China" } ], "current_institution": null, "author_affiliation_indices": [ { "author": "Zhendong Wang", "indices": [ 1 ], 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"institution:db049bef3a2d25e0" ], "source": "curated_admin", "fallback": false } ], "search_institution_ids": [ "institution:e721b03b6f6c172d", "institution:7b971ad17fd639eb", "institution:db049bef3a2d25e0", "institution:73df77aca515d21a" ], "metadata_status": { "overall": "Verified", "default_field_status": "Verified", "field_overrides": { "arxiv": { "status": "Verified", "source": "arXiv" }, "affiliations": { "status": "Curated", "source": "Manual curation" } }, "source": "OpenAlex" } }, { "title": "Does an emotional connection to art really require a human artist? Emotion and intentionality responses to AI- versus human-created art and impact on aesthetic experience", "in_scope": true, "year": 2023, "publication_year": 2023, "publication_date": "2023-07-14", "task": "source_attribution", "paper_categories": [ "method" ], "venue": "Computers in Human Behavior", "venue_name": "Computers in Human Behavior", "venue_type": "journal", "publisher": "Elsevier BV", "publication_type": "journal", "abstract": "AI has captured the artworld, and, increasingly, humans' engagement with many forms of media. Computer-generated art sells for millions at auctions; artists routinely use algorithms to generate aesthetic materials. However, to capture the impact of such works and our relationships with them, we need to better understand the kinds of responses we make to AI/computer-generated images. Here, we consider whether and, if so, to what extent humans report feeling emotions when engaging computer-generated art, or even ascribe intentionality behind those feelings. These are emerging—and also long-standing—points of controversy, with critical arguments that this should not occur, thus marking potential distinctions between artificial and ‘real’ human productions. We tested this by employing visually similar abstract, black-and-white artworks, made by a computer (RNG) or by human artists intentionally aiming at transmitting emotions. In a 2 × 2 design, participants (N = 48) viewed the art, preceded by primes about human/computer provenance (true, 50% of cases). Contrary to critical suggestions, participants almost always reported emotions and ascribed intentionality, independent of the prime given. Interestingly, they did report stronger emotions when the work actually was made by a human. 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To address the challenges, watermark has been leveraged to detect AI-generated content. Specifically, a watermark is embedded into an AI-generated content before it is released. A content is detected as AI-generated if a similar watermark can be decoded from it. In this work, we perform a systematic study on the robustness of such watermark-based AI-generated content detection. We focus on AI-generated images. Our work shows that an attacker can post-process a watermarked image via adding a small, human-imperceptible perturbation to it, such that the post-processed image evades detection while maintaining its visual quality. We show the effectiveness of our attack both theoretically and empirically. Moreover, to evade detection, our adversarial post-processing method adds much smaller perturbations to AI-generated images and thus better maintain their visual quality than existing popular post-processing methods such as JPEG compression, Gaussian blur, and Brightness/Contrast. 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It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of 11.4%. 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"International Conference on Visual Communications and Image Processing", "venue_name": "International Conference on Visual Communications and Image Processing", "venue_type": "conference", "publisher": "", "publication_type": "conference", "abstract": "Creating hyper-realistic synthetic images has become effortless with tremendous development in Generative Artificial Intelligence technologies. Generative Adversarial Networks (GAN) generated synthetic images, especially non-existent face images that are visually indistinguishable from real faces, pose a severe social threat by enabling misinformation dissemination, often over online social networks and through fake social profiles. In spite of successful solutions being reported in the recent literature for detecting GAN-generated synthetic images, the performance of such schemes degrades considerably with the launch of post-processing attacks. In this work, we employ gradient of an image as the key component to detect synthetic images. According to our results, gradient proves to be a considerably efficient image derivative for synthetic image detection as well as to achieve robustness against post-processing attacks. We explore two different gradient operators and design four unique deep learning-based detection networks utilizing different gradient-based feature sets. 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Very recently, methods based on diffusion models (DM) have been gaining the spotlight. In addition to providing an impressive level of photorealism, they enable the creation of text-based visual content, opening up new and exciting opportunities in many different application fields, from arts to video games. On the other hand, this property is an additional asset in the hands of malicious users, who can generate and distribute fake media perfectly adapted to their attacks, posing new challenges to the media forensic community. With this work, we seek to understand how difficult it is to distinguish synthetic images generated by diffusion models from pristine ones and whether current state-of-the-art detectors are suitable for the task. To this end, first we expose the forensics traces left by diffusion models, then study how current detectors, developed for GAN-generated images, perform on these new synthetic images, especially in challenging social-network scenarios involving image compression and resizing. 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Research on active detection & prevention technologies is critical as deepfake continues to evolve. Deepfake has been a blessing, but we've taken advantage of it by utilizing it to swap faces. Deepfake is a new subdomain of Artificial Intelligence (AI) technology in which one person's face is layered over another person's face, which is becoming more and more popular on social networking sites. Deepfake pictures and videos can now be created much more quickly and cheaply due to ML (Machine Learning), which is a primary component of deepfakes. Despite negative connotations attached to the term \"deepfakes,\" technology is increasingly being used in commercial & individual contexts. New technical advancements have made it more difficult to distinguish between deepfakes and images that have been digitally manipulated. The rise of deepfake technologies has sparked a growing sense of unease. The primary goal of this project is to properly distinguish deepfake pictures from real images using deep learning techniques. In this study, we implemented a customized CNN algorithm to identify deepfake pictures from a video dataset and conducted a comparative analysis with two other methods to determine which way was superior. The Kaggle dataset was used to train & test our model. Convolutional neural networks (CNNs) have been used in this research to distinguish authentic & deepfake images by training three distinct CNN models. A customized CNN model, which includes several additional layers such as a dense layer, MaxPooling, as well as a dropout layer, has also been developed and implemented. This method follows the frames extraction, face feature extraction, data preprocessing, and classification phases in determining whether Real or Fake images in the video reflect the objectives. Accuracy, loss, and the area under the receiver operating characteristic (ROC) curve were used to characterize the data. Customized CNN outperformed all other models, achieving 91.4% accuracy, a reduced loss value of 0.342, as well as an AUC of 0.92. 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Diffusion models have advanced to the stage where even non-experts can generate photo-realistic images from a simple text prompt. They expand creative horizons but also open a Pandora's box of potential disinformation risks. In this context, the present corpus of synthetic image detection techniques, primarily focusing on older generative models like Generative Adversarial Networks, finds itself ill-equipped to deal with this emerging trend. Recognizing this challenge, we introduce a method specifically designed to detect synthetic images produced by diffusion models. Our approach capitalizes on the inherent frequency artefacts left behind during the diffusion process. Spectral analysis is used to highlight the artefacts in the Fourier transform of a residual image, which are used to distinguish real from fake images. The proposed method can detect diffusion-model-generated images even under mild JPEG compression, and generalizes relatively well to unknown models. 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They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020, a workshop consulting AI experts from academia, policing, government, the private sector, and state security agencies ranked deepfakes as the most serious AI threat. These experts noted that since fake material can propagate through many uncontrolled routes, changes in citizen behaviour may be the only effective defence. This study aims to assess human ability to identify image deepfakes of human faces (these being uncurated output from the StyleGAN2 algorithm as trained on the FFHQ dataset) from a pool of non-deepfake images (these being random selection of images from the FFHQ dataset), and to assess the effectiveness of some simple interventions intended to improve detection accuracy. Using an online survey, participants (N = 280) were randomly allocated to one of four groups: a control group, and three assistance interventions. Each participant was shown a sequence of 20 images randomly selected from a pool of 50 deepfake images of human faces and 50 images of real human faces. Participants were asked whether each image was AI-generated or not, to report their confidence, and to describe the reasoning behind each response. Overall detection accuracy was only just above chance and none of the interventions significantly improved this. Of equal concern was the fact that participants’ confidence in their answers was high and unrelated to accuracy. Assessing the results on a per-image basis reveals that participants consistently found certain images easy to label correctly and certain images difficult, but reported similarly high confidence regardless of the image. Thus, although participant accuracy was 62% overall, this accuracy across images ranged quite evenly between 85 and 30%, with an accuracy of below 50% for one in every five images. We interpret the findings as suggesting that there is a need for an urgent call to action to address this threat.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1093/cybsec/tyad011", "arxiv_id": "2212.05056", "arxiv_url": "https://arxiv.org/abs/2212.05056", "arxiv_year": 2022, "has_arxiv_version": true, "paper_url": "https://doi.org/10.1093/cybsec/tyad011", "primary_url": "https://doi.org/10.1093/cybsec/tyad011", "landing_page_url": "https://doi.org/10.1093/cybsec/tyad011", "openalex_url": "https://openalex.org/W4381851764", "is_arxiv_preprint": false, "url": "https://doi.org/10.1093/cybsec/tyad011", "authors": [ { "name": "Sergi D. 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Although some existing detectors work well in detecting clean, known GAN samples, their success is largely attributable to overfitting unstable features such as frequency artifacts, which will cause failures when facing unknown GANs or perturbation attacks. To overcome the issue, we propose a robust detection framework based on a novel multi-view image completion representation. The framework first learns various view-to-image tasks to model the diverse distributions of genuine images. Frequency-irrelevant features can be represented from the distributional discrepancies characterized by the completion models, which are stable, generalized, and robust for detecting unknown fake patterns. Then, a multi-view classification is devised with elaborated intra- and inter-view learning strategies to enhance view-specific feature representation and cross-view feature aggregation, respectively. We evaluated the generalization ability of our framework across six popular GANs at different resolutions and its robustness against a broad range of perturbation attacks. The results confirm our method's improved effectiveness, generalization, and robustness over various baselines.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.24963/ijcai.2023/52", "arxiv_id": "2306.01364", "arxiv_url": "https://arxiv.org/abs/2306.01364", "arxiv_year": 2023, "has_arxiv_version": true, "paper_url": "https://doi.org/10.24963/ijcai.2023/52", "primary_url": "https://doi.org/10.24963/ijcai.2023/52", "landing_page_url": "https://doi.org/10.24963/ijcai.2023/52", "openalex_url": "https://openalex.org/W4385767391", "is_arxiv_preprint": false, "url": "https://doi.org/10.24963/ijcai.2023/52", "authors": [ { "name": "Chi Liu", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Tianqing Zhu", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Sheng Shen", "affiliation_indices": [ 2 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Wanlei Zhou", "affiliation_indices": [ 3 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; 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not manually reviewed.", "venue_id": "venue:ijcai", "venue_acronym": "IJCAI", "venue_track": "Main", "venue_label": "International Joint Conference on Artificial Intelligence (IJCAI)", "venue_aliases": [ "International Joint Conference on Artificial Intelligence", "Thirtieth International Joint Conference on Artificial Intelligence", "IJCAI" ], "ambiguity_status": "resolved", "raw_venue": "Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence", "venue_review_required": false, "author_affiliation_counts": { "mapped": 4, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "affiliations": [ { "index": 1, "name": "University of Technology Sydney", "canonical_name": "University of Technology Sydney", "institution_id": "institution:f68896817b1b2d6b", "institution_type": "university", "country": "Australia", "country_code": "", "region": "", "location_display": "Australia" }, { "index": 2, "name": "The University of Sydney", "canonical_name": "The University of Sydney", "institution_id": "institution:1984e5f97afb62d8", "institution_type": "university", "country": "Australia", "country_code": "", "region": "", "location_display": "Australia" }, { "index": 3, "name": "City University of Macau", "canonical_name": "City University of Macau", "institution_id": "institution:769b711ed2fee7c0", "institution_type": "university", "country": "China", "country_code": "CN", "region": "Macau", "location_display": "Macau, China" } ], "current_institution": null, "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:f68896817b1b2d6b", "institution": "University of Technology Sydney", "institution_type": "university", "authors": [ "Chi Liu", "Tianqing Zhu" ], "mapping_source": "raw_affiliation", "mapping_fallback": false }, { "index": 2, "institution_id": "institution:1984e5f97afb62d8", "institution": "The University of Sydney", "institution_type": "university", "authors": [ "Sheng Shen" ], "mapping_source": "raw_affiliation", "mapping_fallback": false }, { "index": 3, "institution_id": "institution:769b711ed2fee7c0", "institution": "City University of Macau", "institution_type": "university", "authors": [ "Wanlei Zhou" ], "mapping_source": "raw_affiliation", "mapping_fallback": false } ], "author_affiliation_indices": [ { "author": "Chi Liu", "indices": [ 1 ], "institution_ids": [ "institution:f68896817b1b2d6b" ], "source": "raw_affiliation", "fallback": false }, { "author": "Tianqing Zhu", "indices": [ 1 ], "institution_ids": [ "institution:f68896817b1b2d6b" ], "source": "raw_affiliation", "fallback": false }, { "author": "Sheng Shen", "indices": [ 2 ], "institution_ids": [ "institution:1984e5f97afb62d8" ], "source": "raw_affiliation", "fallback": false }, { "author": "Wanlei Zhou", "indices": [ 3 ], "institution_ids": [ "institution:769b711ed2fee7c0" ], "source": "raw_affiliation", "fallback": false } ], "author_institution_indices": [ { "author": "Chi Liu", "institution_indices": [ 1 ], "institution_ids": [ "institution:f68896817b1b2d6b" ] }, { "author": "Tianqing Zhu", "institution_indices": [ 1 ], "institution_ids": [ "institution:f68896817b1b2d6b" ] }, { "author": "Sheng Shen", "institution_indices": [ 2 ], "institution_ids": [ "institution:1984e5f97afb62d8" ] }, { "author": "Wanlei Zhou", "institution_indices": [ 3 ], "institution_ids": [ "institution:769b711ed2fee7c0" ] } ], "formal_url": "https://doi.org/10.24963/ijcai.2023/52", "metadata_status": { "overall": "Source metadata", "default_field_status": "Source metadata", "field_overrides": { "arxiv": { "status": "Source metadata", "source": "arXiv" }, "affiliations": { "status": "Needs review" } }, "source": "OpenAlex" } }, { "title": "Towards Universal Fake Image Detectors That Generalize Across Generative Models", "in_scope": true, "year": 2023, "publication_year": 2023, "publication_date": "2023-06-01", "task": "detection", "venue": "IEEE/CVF Conference on Computer Vision and Pattern Recognition", "venue_name": "IEEE/CVF Conference on Computer Vision and Pattern Recognition", "venue_type": "conference", "publisher": "", "publication_type": "conference", "abstract": "With generative models proliferating at a rapid rate, there is a growing need for general purpose fake image detectors. In this work, we first show that the existing paradigm, which consists of training a deep network for real-vs-fake classification, fails to detect fake images from newer breeds of generative models when trained to detect GAN fake images. Upon analysis, we find that the resulting classifier is asymmetrically tuned to detect patterns that make an image fake. The real class becomes a sink class holding anything that is not fake, including generated images from models not accessible during training. Building upon this discovery, we propose to perform real-vs-fake classification without learning; i.e., using a feature space not explicitly trained to distinguish real from fake images. We use nearest neighbor and linear probing as instantiations of this idea. When given access to the feature space of a large pretrained vision-language model, the very simple baseline of nearest neighbor classification has surprisingly good generalization ability in detecting fake images from a wide variety of generative models; e.g., it improves upon the SoTA by +15.07 mAP and +25.90% acc when tested on unseen diffusion and autoregressive models.", "abstract_source": "", "ai_summary": "", "doi": "10.1109/cvpr52729.2023.02345", "arxiv_id": "2302.10174", "arxiv_url": "https://arxiv.org/abs/2302.10174", "arxiv_year": null, "has_arxiv_version": true, "paper_url": "https://doi.org/10.1109/cvpr52729.2023.02345", "primary_url": "https://doi.org/10.1109/cvpr52729.2023.02345", "landing_page_url": "https://doi.org/10.1109/cvpr52729.2023.02345", "openalex_url": "https://openalex.org/W4386071953", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/cvpr52729.2023.02345", "authors": [ { "name": "Utkarsh Ojha", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Yuheng Li", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Yong Jae Lee", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "openalex", "needs_review": false, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review. Imported from a manual import-ready CSV. Strong OpenAlex title match; ready for broad paper-level import. Marker depends on affiliation/coordinates.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_institutions": [ "University of Wisconsin-Madison" ], "aggregated_country_names": [ "United States" ], "aggregated_country_codes": [ "US" ], "aggregated_regions": [ "Wisconsin" ], "aggregated_region_codes": [], "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:3e2e527953176c93", "institution": "University of Wisconsin-Madison", "institution_type": "university", "authors": [ "Utkarsh Ojha", "Yuheng Li", "Yong Jae Lee" ], "mapping_source": "curated_admin", "mapping_fallback": false } ], "author_institution_indices": [ { "author": "Utkarsh Ojha", "institution_indices": [ 1 ], "institution_ids": [ "institution:3e2e527953176c93" ] }, { "author": "Yuheng Li", "institution_indices": [ 1 ], "institution_ids": [ "institution:3e2e527953176c93" ] }, { "author": "Yong Jae Lee", "institution_indices": [ 1 ], "institution_ids": [ "institution:3e2e527953176c93" ] } ], "author_affiliation_indices": [ { "author": "Utkarsh Ojha", "indices": [ 1 ], "institution_ids": [ "institution:3e2e527953176c93" ], "source": "curated_admin", "fallback": false }, { "author": "Yuheng Li", "indices": [ 1 ], "institution_ids": [ "institution:3e2e527953176c93" ], "source": "curated_admin", "fallback": false }, { "author": "Yong Jae Lee", "indices": [ 1 ], "institution_ids": [ "institution:3e2e527953176c93" ], "source": "curated_admin", "fallback": false } ], "paper_id": "curated:1ef55e4fc03eb4880beb", "metadata_source": "openalex", "curation_status": "manually_confirmed", "review_status": "reviewed", "curated_mappings": [ { "mapping_id": "mapping:014b41a09e42e4ae8fa6", "institution_id": "institution:3e2e527953176c93", "location_id": "location:3e2e527953176c93d518", "institution": "University of Wisconsin-Madison", "institution_authors": [ "Utkarsh Ojha", "Yuheng Li", "Yong Jae Lee" ], "raw_affiliation": "University of Wisconsin-Madison", "mapping_status": "active", "affiliation_order": 1 } ], "affiliation_review_state": "curated", "institution_source": "curated", "preliminary_affiliations": false, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "aggregated_locations": [ { "institution_name": "University of Wisconsin-Madison", "institution_id": "institution:3e2e527953176c93", "institution_type": "university", "country": "United States", "country_code": "US", "region": "Wisconsin", "region_code": "", "location_display": "Wisconsin, United States" } ], "aggregated_institution_types": [ "university" ], "venue_id": "venue:cvpr", "venue_acronym": "CVPR", "venue_track": "Main", "raw_venue": "2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)", "venue_aliases": [ "IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)", "IEEE/CVF Conference on Computer Vision and Pattern Recognition", "IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops", "IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops", "IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)", "IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings", "CVPRW", "CVPR", "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)", "2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)" ], "ambiguity_status": "resolved", "venue_label": "IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)", "formal_url": "https://doi.org/10.1109/cvpr52729.2023.02345", "paper_categories": [ "method", "dataset" ], "search_institution_ids": [ "institution:3e2e527953176c93" ], "author_affiliation_counts": { "mapped": 3, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "venue_review_required": false, "metadata_status": { "overall": "Verified", "default_field_status": "Verified", "field_overrides": { "arxiv": { "status": "Verified", "source": "arXiv" }, "affiliations": { "status": "Curated", "source": "Manual curation" } }, "source": "OpenAlex" }, "affiliations": [ { "index": 1, "name": "University of Wisconsin-Madison", "canonical_name": "University of Wisconsin-Madison", "institution_id": "institution:3e2e527953176c93", "institution_type": "university", "country": "United States", "country_code": "", "region": "Wisconsin", "location_display": "Wisconsin, United States" } ], "current_institution": null }, { "title": "TWIGMA: A Dataset of AI-Generated Images with Metadata from Twitter", "in_scope": true, "year": 2023, "publication_year": 2023, "publication_date": "", "task": "detection", "venue": "Advances in Neural Information Processing Systems", "venue_name": "Advances in Neural Information Processing Systems", "venue_type": "conference", "publisher": "European Organization for Nuclear Research", "publication_type": "conference", "abstract": "Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes), available at https://zenodo.org/records/8031785. Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.", "abstract_source": "", "ai_summary": "", "doi": "10.52202/075280-1642", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.52202/075280-1642", "primary_url": "https://doi.org/10.52202/075280-1642", "landing_page_url": "https://doi.org/10.5281/zenodo.8031784", "openalex_url": "https://openalex.org/W4393610334", "is_arxiv_preprint": false, "url": "https://doi.org/10.52202/075280-1642", "authors": [ { "name": "Yiqun T. Chen", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "canonical_author_mapping", "affiliation_fallback": true, "affiliation_status": "mapped" }, { "name": "James Zou", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "canonical_author_mapping", "affiliation_fallback": true, "affiliation_status": "mapped" } ], "source_database": "openalex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review. Imported from a manual import-ready CSV. Strong OpenAlex title match; ready for broad paper-level import. Marker depends on affiliation/coordinates.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_institutions": [ "Stanford University" ], "aggregated_country_names": [ "United States" ], "aggregated_country_codes": [ "US" ], "aggregated_regions": [], "aggregated_region_codes": [], "affiliations": [ { "index": 1, "name": "Stanford University", "canonical_name": "Stanford University", "institution_id": "institution:ed72534d79b73fac", "institution_type": "university", "country": "United States", "country_code": "", "region": "", "location_display": "United States", "preliminary": true } ], "current_institution": null, "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:ed72534d79b73fac", "institution": "Stanford University", "institution_type": "university", "authors": [ "Yiqun T. 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Chen", "indices": [ 1 ], "institution_ids": [ "institution:ed72534d79b73fac" ], "source": "canonical_author_mapping", "fallback": true }, { "author": "James Zou", "indices": [ 1 ], "institution_ids": [ "institution:ed72534d79b73fac" ], "source": "canonical_author_mapping", "fallback": true } ], "affiliation_review_state": "unreviewed", "institution_source": "automatic_fallback", "preliminary_affiliations": true, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "aggregated_locations": [ { "institution_name": "Stanford University", "institution_id": "institution:ed72534d79b73fac", "institution_type": "university", "country": "United States", "country_code": "US", "region": "", "region_code": "", "location_display": "United States" } ], "aggregated_institution_types": [ "university" ], "venue_id": "venue:neurips", "venue_acronym": "NeurIPS", "venue_track": "Main", "raw_venue": "Zenodo (CERN European Organization for Nuclear Research)", "venue_aliases": [ "Advances in Neural Information Processing Systems", "NeurIPS" ], "ambiguity_status": "resolved", "venue_label": "Advances in Neural Information Processing Systems (NeurIPS)", "paper_id": "curated:a99dc95e4b7a85bd938f", "metadata_source": "openalex", "curation_status": "corrected_by_admin", "review_status": "reviewed", "formal_url": "https://doi.org/10.52202/075280-1642", "paper_categories": [ "dataset" ], "author_affiliation_counts": { "mapped": 2, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "venue_review_required": false, "metadata_status": { "overall": "Verified", "default_field_status": "Verified", "field_overrides": { "affiliations": { "status": "Needs review" } }, "source": "OpenAlex" } }, { "title": "Universal Detection and Source Attribution of Diffusion Model Generated Images with High Generalization and Robustness", "in_scope": true, "year": 2023, "publication_year": 2023, "publication_date": "2023-01-01", "task": "detection_and_source_attribution", "publisher": "Springer Science+Business Media", "publication_type": "conference", "abstract": "The proliferation of synthetic media over the internet is posing a significant social threat. Recent advancements in Diffusion Models (DM) have made it easier to create astonishingly photo-realistic synthetic media with high stability and control. Moreover, applications like DALLE-2, powered by DM and Large Language Models (LLM), permit visual content generation from natural language description, enabling opportunities for everyone to generate visual media. Hence, there is an immediate need to identify synthetic images and attribute them to their source architectures. In this work, we propose a synthetic image detector as universal detector and a source model attributor based on a popular transfer-learning model ResNet-50 and compare the results with other popular models, including Visual Geometry Group (VGG) 16, XceptionNet and InceptionNet. The proposed universal detector attains over 96% accuracy, with a source attribution, accuracy over 93% for detection of Diffusion Model generated images. 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The primary aim of our research study is to detect deepfake media using an efficient framework. A novel deepfake predictor (DFP) approach based on a hybrid of VGG16 and convolutional neural network architecture is proposed in this study. The deepfake dataset based on real and fake faces is utilized for building neural network techniques. The Xception, NAS-Net, Mobile Net, and VGG16 are the transfer learning techniques employed in comparison. The proposed DFP approach achieved 95% precision and 94% accuracy for deepfake detection. Our novel proposed DFP approach outperformed transfer learning techniques and other state-of-the-art studies. 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Accordingly, solutions to effectively detect road accidents can be a game-changer for road safety. The focus is on Vision Zero, i.e., eliminate all traffic fatalities and severe injuries. However, when an accident occurs, response time is essential. Several countries have surveillance cameras monitoring the road network. However, real-time monitoring requires a considerable number of people with training and attention. Our solution automates this with AI, using deep learning to train a model to detect potential accidents. The CV method based on binary image classification was used, being images without accidents classified as normal and images with accidents as alarm. The biggest challenge in developing the model was obtaining images of accidents from the analyzed cameras. The solution adopted was to create synthetic images of these rare events. Several architectures of Convolutional Neural Networks were tested, and it was found that the best approach was to use transfer learning. 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Explainability is the first step toward achieving transparency, but the existing incapacity of DL to explain its own decisions to human users limits the efficacy of these systems. Though Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of these systems. This work proposes to provide a comprehensive study of deepfake detection using the DL method and analyze the result of the most effective algorithm with Local Interpretable Model-Agnostic Explanations (LIME) to assure its validity and reliability. This study identifies real and deepfake images using different Convolutional Neural Network (CNN) models to get the best accuracy. It also explains which part of the image caused the model to make a specific classification using the LIME algorithm. To apply the CNN model, the dataset is taken from Kaggle, which includes 70 k real images from the Flickr dataset collected by Nvidia and 70 k fake faces generated by StyleGAN of 256 px in size. For experimental results, Jupyter notebook, TensorFlow, NumPy, and Pandas were used as software, InceptionResnetV2, DenseNet201, InceptionV3, and ResNet152V2 were used as CNN models. All these models’ performances were good enough, such as InceptionV3 gained 99.68% accuracy, ResNet152V2 got an accuracy of 99.19%, and DenseNet201 performed with 99.81% accuracy. However, InceptionResNetV2 achieved the highest accuracy of 99.87%, which was verified later with the LIME algorithm for XAI, where the proposed method performed the best. 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In this situation, the task of image forensics is urgent. The existing methods examine generated images and claim a forgery by detecting visual artifacts or invisible patterns, resulting in generalization issues. We observed that the noise pattern of real images exhibits similar characteristics in the frequency domain, while the generated images are far different. Therefore, we can perform image authentication by checking whether an image follows the patterns of authentic images. The experiments show that a simple classifier using noise patterns can easily detect a wide range of generative models, including GAN and flow-based models. Our method achieves state-of-the-art performance on both low- and high-resolution images from a wide range of generative models and shows superior generalization ability to unseen models. 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It is imperative for media forensics to develop detectors to expose them accurately. Existing detection methods have shown high performance in generated images detection, but they tend to generalize poorly in the real-world scenarios, where the synthetic images are usually generated with unseen models using unknown source data. In this work, we emphasize the importance of combining information from the whole image and informative patches in improving the generalization ability of AI-synthesized image detection. Specifically, we design a two-branch model to combine global spatial information from the whole image and local informative features from multiple patches selected by a novel patch selection module. Multi-head attention mechanism is further utilized to fuse the global and local features. We collect a highly diverse dataset synthesized by 19 models with various objects and resolutions to evaluate our model. 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To resolve this issue, several fake image detectors based on supervised binary classification have been designed. However, current methods remain vulnerable when testing samples are gener-ated by an unknown GAN model. In this work, an unsuper-vised domain adaptation strategy is introduced to improve the performance in the generalization of GAN-generated image detection by using a small number of unlabeled images from the target domain. Self-Attention block and novel loss function have been constructed to optimize the domain adaptation process, thus getting a better generalization. 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However, the current deepfake detection algorithms generally have the problem of poor generalization, and the accuracy rate drops sharply on datasets with unknown deepfake methods. In this paper, we propose a facial fusion algorithm called MaskGAN to enable more generalized deepfake detection. The generator of MaskGAN uses U-Net and SSE to extract the features of face images, and realizes mask generation and face fusion, The discriminator of MaskGAN uses the convolution layer to discriminate the face-swaping images generated by MaskGAN. Then, the obtained face-swaping images are used as training sets and input into an improved Deeplab V3+for training, so that the network can extract the fusion feature circle generated during the face-swaping process from the face-swaping images, so as to identify the authenticity of the face-swaping images. We achieve accurate face swapping with only fused features introduced, generating a face swapping dataset with fused labels. It solves the common problems of over-fitting and poor generalization of existing algorithms. 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detecting whether an image is synthetic and, if so, determining which GAN architecture created it. Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in quality, resolution, shape, etc.) commonly encountered as images are re-shared online. In order to formalize our research, a chal\u0002lenging benchmark, Attribution88, is collected for robust and practical image attribution. We then propose RepMix, our GAN fingerprinting technique based on representation mixing and a novel loss. We validate its capability of tracing the provenance of GAN-generated images invari\u0002ant to the semantic content of the image and also robust to perturbations. We show our approach improves significantly from existing GAN finger\u0002printing works on both semantic generalization and robustness. 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Additionally, we describe the limitations of synthetic image recognition methods in practice, the counter‐forensic attacks devised to exploit these limitations, and directions for new research to assure the long‐term relevance of deepfake forensics. Reliable, explainable, and generalizable attribution methods would hold malicious users accountable for AI‐enabled disinformation, grant plausible deniability to appropriate users, and facilitate intellectual property protection of deepfake technology. 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The synthesized fake images spread widely on the Internet, and it is challenging for Internet users to identify the authenticity, which poses huge security risk to the society. However, compared with the powerful image synthesis technology, the detection of GAN-synthesized images is still in its infancy and face a variety of challenges. In this study, a method named fake images discriminator (FID) is proposed, which detects that GAN-synthesized fake images use the strong spectral correlation in the imaging process of natural color images. The proposed method first converts the color image into three color components of R, G, and B. Discrete wavelet transform (DWT) is then applied to RGB components separately. Finally, the correlation coefficient between the subband images is used as a feature vector for authenticity classification. Experimental results show that the proposed FID method achieves impressive effectiveness on the StyleGAN2-synthesized faces and multitype fake images synthesized with the state-of-the-art GANs. 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Deepfake takes it to a whole new level. This deep learning based technology creates new images with features which have been acquired from a different set of images. The rapid evolution of Generative Adversarial networks (GANs) provides an available route to create deepfakes. They generate highly sophisticated and realistic images through deep learning and implement deepfake using image-to-image translation. We propose a novel, memory-efficient lightweight machine learning based deepfake detection method which is successfully deployed in the IoT platform. A detection API is proposed along with the detection method. To the best of the authors’ knowledge, this effort is the first ever for detecting highly sophisticated GAN generated deepfake images at the edge. The novelty of the work is achieving a considerable amount of accuracy with a short training time and inference at the edge device. The total time for sending the image to the edge, detecting and result display through the API is promising. Some discussion is also provided to improve accuracy and to reduce the inference time. A comparative study is also made by performing a three-fold textural analysis - computation of Shannon’s entropy, measurement of some of Haralick’s texture features (like contrast, dissimilarity, homogeneity, correlation) and study of the histograms of the generated images. 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In order to effectively train a model in the case of insufficient tampered images, we design a training data generation strategy by resorting to Photoshop scripting, which can imitate human manipulations and generate large-scale training samples. Extensive experimental results show that the proposed approach outperforms state-of-the-art competitors when the model is trained with only generated images or fine-tuned with a small amount of realistic tampered images. 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Existing works on fake image attribution mainly rely on a direct classification framework. Without additional supervision, the extracted features could include many content-relevant components and generalize poorly. Meanwhile, how to obtain an interpretable GAN fingerprint to explain the decision remains an open question. Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Network (GFD-Net) to simultaneously disentangle the fingerprint from GAN-generated images and produce a content-irrelevant representation for fake image attribution. A series of constraints are provided to guarantee the stability and discriminability of the fingerprint, which in turn helps content-irrelevant feature extraction. Further, we perform comprehensive analysis on GAN fingerprint, providing some clues about the properties of GAN fingerprint and which factors dominate the fingerprint in GAN architecture. 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Existing works have shown to attribute images to their corresponding GAN sources with high accuracy. However, these works are limited to a closed set scenario, failing to generalize to GANs unseen during train time and are therefore, not scalable with a steady influx of new GANs. We present an iterative algorithm for discovering images generated from previously unseen GANs by exploiting the fact that all GANs leave distinct fingerprints on their generated images. Our algorithm consists of multiple components including network training, out-of-distribution detection, clustering, merge and refine steps. Through extensive experiments, we show that our algorithm discovers unseen GANs with high accuracy and also generalizes to GANs trained on unseen real datasets. We additionally apply our algorithm to attribution and discovery of GANs in an online fashion as well as to the more standard task of real/fake detection. 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"arxiv": { "status": "Source metadata", "source": "arXiv" }, "affiliations": { "status": "Curated", "source": "OpenAlex" } }, "source": "OpenAlex" } }, { "title": "VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents", "in_scope": true, "year": 2021, "publication_year": 2021, "publication_date": "2021-03-08", "task": "detection", "paper_categories": [ "dataset" ], "venue": "Journal of Imaging", "venue_name": "Journal of Imaging", "venue_type": "journal", "publisher": "Multidisciplinary Digital Publishing Institute", "publication_type": "journal", "abstract": "The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.3390/jimaging7030050", "arxiv_id": "", "arxiv_url": "", "arxiv_year": null, "has_arxiv_version": false, "paper_url": "https://doi.org/10.3390/jimaging7030050", "primary_url": "https://doi.org/10.3390/jimaging7030050", "landing_page_url": "https://doi.org/10.3390/jimaging7030050", "openalex_url": "https://openalex.org/W3134971133", "is_arxiv_preprint": false, "url": "https://doi.org/10.3390/jimaging7030050", "authors": [ { "name": "Anselmo Ferreira", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Ehsan Nowroozi", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Mauro Barni", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_locations": [ { "institution_name": "University of Siena", "institution_id": "institution:65dbfd1e022f02e2", "institution_type": "university", "country": "Italy", "country_code": "IT", "region": "", "region_code": "", "location_display": "Italy" } ], "aggregated_institutions": [ "University of Siena" ], "aggregated_institution_types": [ "university" ], "aggregated_country_names": [ "Italy" ], "aggregated_country_codes": [ "IT" ], "aggregated_regions": [], "aggregated_region_codes": [], "affiliation_review_state": "unreviewed", "institution_source": "automatic_fallback", "preliminary_affiliations": true, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "venue_id": "venue:journal-of-imaging", "venue_acronym": "J. 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As a result, adversaries are attracted to using GAN-generated fake images for disinformation campaigns and fraud on social networks. However, training an image generation network to produce realistic-looking samples remains a time-consuming and difficult problem, so adversaries are more likely to use published GAN models to generate fake images. In this paper, we analyze the frequency domain to attribute and detect fake images generated by a known GAN model. We derive a similarity metric on the frequency domain and develop a new approach for GAN image attribution. We conduct experiments on four trained GAN models and two real image datasets. Our results show high attribution accuracy against real images and those from other GAN models. We further analyze our method under evasion attempts and find the frequency-based approach is comparatively robust. In this paper, we analyze the frequency domain to attribute and detect fake images generated by a known GAN model. We derive a similarity metric on the frequency domain and develop a new approach for GAN image attribution. We conduct experiments on four trained GAN models and two real image datasets. Our results show high attribution accuracy against real images and those from other GAN models. 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"openalex", "metadata_source": "openalex", "curation_status": "manually_confirmed", "review_status": "reviewed", "needs_review": false, "notes": "", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_institutions": [ "University of Texas at Dallas" ], "aggregated_country_names": [ "United States" ], "aggregated_country_codes": [ "US" ], "aggregated_regions": [ "Texas" ], "aggregated_region_codes": [], "curated_mappings": [ { "mapping_id": "mapping:5958624e4e80750d8bef", "institution_id": "institution:a55a77635edc8711", "location_id": "", "institution": "University of Texas at Dallas", "institution_authors": [ "Matthew Joslin", "Shuang Hao" ], "raw_affiliation": "", "mapping_status": "active", "affiliation_order": 1 } ], "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:a55a77635edc8711", "institution": "University of Texas at Dallas", "institution_type": 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"country": "United States", "country_code": "US", "region": "Texas", "region_code": "", "location_display": "Texas, United States" } ], "aggregated_institution_types": [ "university" ], "raw_venue": "2020 IEEE Security and Privacy Workshops (SPW)", "ambiguity_status": "resolved", "formal_url": "https://doi.org/10.1109/spw50608.2020.00019", "paper_categories": [ "method" ], "search_institution_ids": [ "institution:a55a77635edc8711" ], "author_affiliation_counts": { "mapped": 2, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "venue_name": "IEEE Symposium on Security and Privacy", "venue_id": "venue:ieee-symposium-on-security-and-privacy", "venue_acronym": "IEEE S&P", "venue_type": "conference", "venue_track": "Workshop", "venue_label": "IEEE Symposium on Security and Privacy (IEEE S&P) · Workshop", "venue_aliases": [ "IEEE Symposium on Security and Privacy", "IEEE Security and Privacy", "2020 IEEE Security and Privacy Workshops (SPW)", "SPW", "IEEE S&P", "2024 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"venue_name": "IEEE International Workshop on Information Forensics and Security", "venue_type": "conference", "publisher": "Cornell University", "publication_type": "conference", "abstract": "Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natural images thus contributing to preserve the trustworthiness of digital images. While modern GAN models can generate very high-quality images with no visible spatial artifacts, reconstruction of consistent relationships among colour channels is expectedly more difficult. In this paper, we propose a method for distinguishing GAN-generated from natural images by exploiting inconsistencies among spectral bands, with specific focus on the generation of synthetic face images. Specifically, we use cross-band co-occurrence matrices, in addition to spatial co-occurrence matrices, as input to a CNN model, which is trained to distinguish between real and synthetic faces. The results of our experiments confirm the goodness of our approach which outperforms a similar detection technique based on intra-band spatial co-occurrences only. The performance gain is particularly significant with regard to robustness against post-processing, like geometric transformations, filtering and contrast manipulations.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/wifs49906.2020.9360905", "arxiv_id": "2007.12909", "arxiv_url": "https://arxiv.org/abs/2007.12909", "arxiv_year": 2020, "has_arxiv_version": true, "paper_url": "https://doi.org/10.1109/wifs49906.2020.9360905", "primary_url": "https://doi.org/10.1109/wifs49906.2020.9360905", "landing_page_url": "https://doi.org/10.1109/wifs49906.2020.9360905", "openalex_url": "https://openalex.org/W3044151627", "is_arxiv_preprint": false, "url": "https://doi.org/10.1109/wifs49906.2020.9360905", "authors": [ { "name": "Mauro Barni", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Kassem Kallas", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Ehsan Nowroozi", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Benedetta Tondi", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "raw_affiliation", "affiliation_fallback": false, "affiliation_status": "mapped" } ], "source_database": "OpenAlex", "needs_review": true, "notes": "Automatically extracted OpenAlex candidate; all metadata and preliminary labels require manual review.", "has_map_location": true, "map_record_count": 1, "missing_affiliation": false, "missing_coordinates": false, "coverage_status": "map_ready", "aggregated_locations": [ { "institution_name": "University of Siena", "institution_id": "institution:65dbfd1e022f02e2", "institution_type": "university", "country": "Italy", "country_code": "IT", "region": "", "region_code": "", "location_display": "Italy" } ], "aggregated_institutions": [ "University of Siena" ], "aggregated_institution_types": [ "university" ], "aggregated_country_names": [ "Italy" ], "aggregated_country_codes": [ "IT" ], "aggregated_regions": [], "aggregated_region_codes": [], "affiliation_review_state": "unreviewed", "institution_source": "automatic_fallback", "preliminary_affiliations": true, "resolution_notes": "Preliminary automatic affiliation evidence; not manually reviewed.", "venue_track": "Workshop", "venue_id": "venue:wifs", "venue_acronym": "WIFS", "venue_label": "IEEE International Workshop on Information Forensics and Security (WIFS)", "venue_aliases": [ "IEEE International Workshop on Information Forensics and Security (WIFS)", "IEEE International Workshop on Information Forensics and Security", "WIFS", "2020 IEEE International Workshop on Information Forensics and Security (WIFS)", "2024 IEEE International Workshop on Information Forensics and Security (WIFS)", "2019 IEEE International Workshop on Information Forensics and Security (WIFS)" ], "ambiguity_status": "unresolved", "raw_venue": "2020 IEEE International Workshop on Information Forensics and Security (WIFS)", "venue_review_required": true, "venue_review_reason": "This is a standalone scholarly workshop venue. Its name alone does not establish a Workshop subtrack; confirm Main versus an actual nested workshop track from the paper/program, preserving the venue identity.", "author_affiliation_counts": { "mapped": 4, "non_institutional": 0, "unresolved": 0 }, "affiliation_complete": true, "affiliations": [ { "index": 1, "name": "University of Siena", "canonical_name": "University of Siena", "institution_id": "institution:65dbfd1e022f02e2", "institution_type": "university", "country": "Italy", "country_code": "", "region": "", "location_display": "Italy" } ], "current_institution": null, "author_institution_affiliations": [ { "index": 1, "institution_id": "institution:65dbfd1e022f02e2", "institution": "University of Siena", "institution_type": "university", "authors": [ "Mauro Barni", "Kassem Kallas", "Ehsan Nowroozi", "Benedetta Tondi" ], "mapping_source": "raw_affiliation", "mapping_fallback": false } ], "author_affiliation_indices": [ { "author": "Mauro Barni", "indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ], "source": "raw_affiliation", "fallback": false }, { "author": "Kassem Kallas", "indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ], "source": "raw_affiliation", "fallback": false }, { "author": "Ehsan Nowroozi", "indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ], "source": "raw_affiliation", "fallback": false }, { "author": "Benedetta Tondi", "indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ], "source": "raw_affiliation", "fallback": false } ], "author_institution_indices": [ { "author": "Mauro Barni", "institution_indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ] }, { "author": "Kassem Kallas", "institution_indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ] }, { "author": "Ehsan Nowroozi", "institution_indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ] }, { "author": "Benedetta Tondi", "institution_indices": [ 1 ], "institution_ids": [ "institution:65dbfd1e022f02e2" ] } ], "formal_url": "https://doi.org/10.1109/wifs49906.2020.9360905", "metadata_status": { "overall": "Source metadata", "default_field_status": "Source metadata", "field_overrides": { "venue": { "status": "Needs review", "source": "OpenAlex" }, "arxiv": { "status": "Source metadata", "source": "arXiv" }, "affiliations": { "status": "Needs review" } }, "source": "OpenAlex" } }, { "title": "CNN-Generated Images Are Surprisingly Easy to Spot… for Now", "in_scope": true, "year": 2020, "publication_year": 2020, "publication_date": "2020-06-01", "task": "detection", "venue": "IEEE/CVF Conference on Computer Vision and Pattern Recognition", "venue_name": "IEEE/CVF Conference on Computer Vision and Pattern Recognition", "venue_type": "conference", "publisher": "", "publication_type": "conference", "abstract": "In this work we ask whether it is possible to create a \"universal\" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN, StarGAN, GauGAN, DeepFakes, cascaded refinement networks, implicit maximum likelihood estimation, second-order attention super-resolution, seeing-in-the-dark). We demonstrate that, with careful pre- and post-processing and data augmentation, a standard image classifier trained on only one specific CNN generator (ProGAN) is able to generalize surprisingly well to unseen architectures, datasets, and training methods (including the just released StyleGAN2). 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Its rapid development causes people's panic and reflection. In this paper we presents an improved VGG network named NA-VGG to detect DeepFake face image, which was based on image noise and image augmentation. Firstly, In order to learn the tampering artifacts that may not be seen in RGB channels, SRM filter layer is used to highlight the image noise features; Secondly, the image noise map is augmented to weaken the face features. Finally, the augmented noise images are input into the network to train and judge whether the image is forged. 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Image is an important carrier for people to get information on a daily basis. However the spread of CG produced for malicious purposes may disrupt social order and even undermine social stability. Therefore, the accurate detection of CG and PG is of great significance. In this paper, we (1) introduce 11 approaches that apply deep learning to the implementations of CG detection, and divide them into 4 categories based on the network structure; (2) give an introduction to the available datasets; (3) design a series of experiments to test the detection performance of each approach,then analyze the experimental results; The experimental results show that most approaches can differentiate CG from PG, while the detection accuracy and efficiency of each model are different. Nevertheless none of these methods is valid when the images tampered by noise. 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But they also raise challenges to visual forensics and model attribution. We present the first study of learning GAN fingerprints towards image attribution and using them to classify an image as real or GAN-generated. For GAN-generated images, we further identify their sources. Our experiments show that (1) GANs carry distinct model fingerprints and leave stable fingerprints in their generated images, which support image attribution; (2) even minor differences in GAN training can result in different fingerprints, which enables fine-grained model authentication; (3) fingerprints persist across different image frequencies and patches and are not biased by GAN artifacts; (4) fingerprint finetuning is effective in immunizing against five types of adversarial image perturbations; and (5) comparisons also show our learned fingerprints consistently outperform several baselines in a variety of setups.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.1109/iccv.2019.00765", "arxiv_id": "1811.08180", "arxiv_url": "https://arxiv.org/abs/1811.08180", "arxiv_year": 2018, "has_arxiv_version": true, "paper_url": "https://doi.org/10.1109/iccv.2019.00765", "primary_url": "https://doi.org/10.1109/iccv.2019.00765", "landing_page_url": 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"curated_admin", "fallback": false }, { "author": "Mario Fritz", "indices": [ 3 ], "institution_ids": [ "institution:aea376936c88135b" ], "source": "curated_admin", "fallback": false } ], "search_institution_ids": [ "institution:13f7c73ae8019e75", "institution:bef37c90751e824e", "institution:aea376936c88135b" ], "metadata_status": { "overall": "Verified", "default_field_status": "Verified", "field_overrides": { "arxiv": { "status": "Verified", "source": "arXiv" }, "affiliations": { "status": "Curated", "source": "Manual curation" } }, "source": "OpenAlex" } }, { "title": "Complement Face Forensic Detection and Localization with FacialLandmarks", "in_scope": true, "year": 2019, "publication_year": 2019, "publication_date": "2019-10-12", "task": "detection", "paper_categories": [ "method" ], "venue": "arXiv", "venue_name": "arXiv", "venue_type": "preprint", "publisher": "Cornell University", "publication_type": "preprint", "abstract": "Recently, Generative Adversarial Networks (GANs) and 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Although there have been some prior works that tackle face forensic classification problem, it is not trivial to estimate edited locations from classification predictions. In this paper, we propose, to the best of our knowledge, the first rigorous face forensic localization dataset, which consists of genuine, generated, and manipulated face images. In particular, the pristine parts contain face images from CelebA and FFHQ datasets. The fake images are generated from various GANs methods, namely DCGANs, LSGANs, BEGANs, WGAN-GP, ProGANs, and StyleGANs. Lastly, the edited subset is generated from StarGAN and SEFCGAN based on free-form masks. In total, the dataset contains about 1.3 million facial images labelled with corresponding binary masks. Based on the proposed dataset, we demonstrated that explicit adding facial landmarks information in addition to input images improves the performance. In addition, our proposed method consists of two branches and can coherently predict face forensic detection and localization to outperform the previous state-of-the-art techniques on the newly proposed dataset as well as the faceforecsic++ dataset especially on low-quality videos.", "abstract_source": "OpenAlex local raw cache", "ai_summary": "", "doi": "10.48550/arxiv.1910.05455", "arxiv_id": "1910.05455", "arxiv_url": "https://arxiv.org/abs/1910.05455", "arxiv_year": 2019, "has_arxiv_version": true, "paper_url": "", "primary_url": "https://openalex.org/W2979433881", "landing_page_url": "http://arxiv.org/abs/1910.05455", "openalex_url": "https://openalex.org/W2979433881", "is_arxiv_preprint": true, "url": "https://openalex.org/W2979433881", "authors": [ { "name": "Kritaphat Songsri-in", "affiliation_indices": [ 1 ], "is_current_marker_author": false, "affiliation_source": "curated_admin", "affiliation_fallback": false, "affiliation_status": "mapped" }, { "name": "Stefanos Zafeiriou", 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While GANs have been shown to be helpful in a number of computer vision applications, there are other problematic uses such as `deep fakes' which necessitate such forensics. Source camera attribution algorithms using various cues have addressed this need for imagery captured by a camera, but there are fewer options for synthetic imagery. We address the problem of attributing a synthetic image to a specific generator in a white box setting, by inverting the process of generation. 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