Social gaze consistency between interacting people is proposed as a new semantic cue orthogonal to low-level artifacts for detecting AI-generated images, with reported accuracy gains on vision and vision-language models.
Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection
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CAM-VFD detects video forgeries by using cross-attention to identify contradictions between CLIP appearance, VideoMAE motion, and MiDaS depth features.
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
The ITW-SM dataset and targeted optimization of detector design choices yield a 26.87% average AUC improvement for state-of-the-art AI-generated image detectors under real-world social media conditions.
citing papers explorer
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When Eyes Betray AI: Social Gaze Consistency as a Semantic Cue for AI-Generated Image Detection
Social gaze consistency between interacting people is proposed as a new semantic cue orthogonal to low-level artifacts for detecting AI-generated images, with reported accuracy gains on vision and vision-language models.
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CAM-VFD: Cross-Attention Multimodal Video Forgery Detection
CAM-VFD detects video forgeries by using cross-attention to identify contradictions between CLIP appearance, VideoMAE motion, and MiDaS depth features.
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Deepfake Detection Generalization with Diffusion Noise
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
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Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters?
The ITW-SM dataset and targeted optimization of detector design choices yield a 26.87% average AUC improvement for state-of-the-art AI-generated image detectors under real-world social media conditions.