How Noise Benefits AI-generated Image Detection

• URL: http://arxiv.org/abs/2511.16136v1
• Date: Thu, 20 Nov 2025 08:16:24 GMT
• Title: How Noise Benefits AI-generated Image Detection
• Authors: Jiazhen Yan, Ziqiang Li, Fan Wang, Kai Zeng, Zhangjie Fu,
• Abstract summary: Out-of-distribution generalization of AI-generated images is a persistent challenge.<n>We propose the Positive-Incentive Noise for CLIP (PiN-CLIP), which jointly trains a noise generator and a detection network under a variational positive-incentive principle.<n>Our method achieves new state-of-the-art performance, with notable improvements of 5.4 in average accuracy over existing approaches.
• Score: 15.496270003630451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of generative models has made real and synthetic images increasingly indistinguishable. Although extensive efforts have been devoted to detecting AI-generated images, out-of-distribution generalization remains a persistent challenge. We trace this weakness to spurious shortcuts exploited during training and we also observe that small feature-space perturbations can mitigate shortcut dominance. To address this problem in a more controllable manner, we propose the Positive-Incentive Noise for CLIP (PiN-CLIP), which jointly trains a noise generator and a detection network under a variational positive-incentive principle. Specifically, we construct positive-incentive noise in the feature space via cross-attention fusion of visual and categorical semantic features. During optimization, the noise is injected into the feature space to fine-tune the visual encoder, suppressing shortcut-sensitive directions while amplifying stable forensic cues, thereby enabling the extraction of more robust and generalized artifact representations. Comparative experiments are conducted on an open-world dataset comprising synthetic images generated by 42 distinct generative models. Our method achieves new state-of-the-art performance, with notable improvements of 5.4 in average accuracy over existing approaches.

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