All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning

• URL: http://arxiv.org/abs/2504.01396v1
• Date: Wed, 02 Apr 2025 06:32:09 GMT
• Title: All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning
• Authors: Zheng Yang, Ruoxin Chen, Zhiyuan Yan, Ke-Yue Zhang, Xinghe Fu, Shuang Wu, Xiujun Shu, Taiping Yao, Junchi Yan, Shouhong Ding, Xi Li,
• Abstract summary: We establish two key principles for AIGI detection through systematic analysis.<n>textbf(1) All Patches Matter: Unlike conventional image classification where discriminative features concentrate on object-centric regions, each patch in AIGIs inherently contains synthetic artifacts due to the uniform generation process.<n>textbf (2) More Patches Better: Leveraging distributed artifacts across more patches improves detection by capturing complementary forensic evidence.<n>textbfPanoptic textbfPatch textbfLearning (PPL) framework.
• Score: 76.79222779026634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exponential growth of AI-generated images (AIGIs) underscores the urgent need for robust and generalizable detection methods. In this paper, we establish two key principles for AIGI detection through systematic analysis: \textbf{(1) All Patches Matter:} Unlike conventional image classification where discriminative features concentrate on object-centric regions, each patch in AIGIs inherently contains synthetic artifacts due to the uniform generation process, suggesting that every patch serves as an important artifact source for detection. \textbf{(2) More Patches Better}: Leveraging distributed artifacts across more patches improves detection robustness by capturing complementary forensic evidence and reducing over-reliance on specific patches, thereby enhancing robustness and generalization. However, our counterfactual analysis reveals an undesirable phenomenon: naively trained detectors often exhibit a \textbf{Few-Patch Bias}, discriminating between real and synthetic images based on minority patches. We identify \textbf{Lazy Learner} as the root cause: detectors preferentially learn conspicuous artifacts in limited patches while neglecting broader artifact distributions. To address this bias, we propose the \textbf{P}anoptic \textbf{P}atch \textbf{L}earning (PPL) framework, involving: (1) Random Patch Replacement that randomly substitutes synthetic patches with real counterparts to compel models to identify artifacts in underutilized regions, encouraging the broader use of more patches; (2) Patch-wise Contrastive Learning that enforces consistent discriminative capability across all patches, ensuring uniform utilization of all patches. Extensive experiments across two different settings on several benchmarks verify the effectiveness of our approach.

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