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

Abstract

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.