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

The rapid proliferation of AI-generated images (AIGIs) highlights the pressing demand for generalizable detection methods. In this paper, we establish two key principles for AIGI detection task through systematic analysis: (1) All Patches Matter, since the uniform generation process ensures that each patch inherently contains synthetic artifacts, making every patch a valuable detection source; and (2) More Patches Better, as leveraging distributed artifacts across more patches improves robustness by reducing over-reliance on specific regions. However, counterfactual analysis uncovers a critical weakness: naively trained detectors display Few-Patch Bias, relying disproportionately on minority patches. We identify this bias to Lazy Learner effect, where detectors to limited patch artifacts while neglecting distributed cues. 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. Extensive experiments demonstrate that PPL enhances generalization and robustness across datasets.