MAFL uses adversarial training to suppress pattern and content biases, guiding models to learn shared generative features for better cross-model generalization in detecting AI images.
Faceforensics++: Learning to detect manipulated facial images
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The proposed steganography-based attribution system with CLIP multimodal fusion achieves robust watermarking under distortions and 0.99 AUC-ROC for harm detection, enabling traceable AI content accountability.
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Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection
MAFL uses adversarial training to suppress pattern and content biases, guiding models to learn shared generative features for better cross-model generalization in detecting AI images.
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Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection
The proposed steganography-based attribution system with CLIP multimodal fusion achieves robust watermarking under distortions and 0.99 AUC-ROC for harm detection, enabling traceable AI content accountability.