PhysCons: Physics-Aware Consistency Modeling for Robust AI-Generated Image Detection

Detecting AI-generated images from unseen generators remains challenging, as existing methods often overfit to generator-specific artifacts that evolve rapidly. We observe that while generative models change over time, the physical laws governing real-world image formation—such as optics, sensor noise, and illumination—remain invariant. Based on this observation, we propose PhysCons, a physics-aware consistency modeling framework that detects synthetic images by identifying violations of intrinsic physical consistency, rather than modeling generator-dependent signatures. PhysCons jointly exploits three coupled physical dimensions—spectral–spatial structure, luminance–chrominance coupling, and noise–texture statistics—to expose the physical decoupling inherent in generative processes. Extensive experiments on GenImage and AIGCDetectBenchmark demonstrate state-of-the-art generalization of PhysCons to unseen generators. Notably, PhysCons achieves this performance with only 0.05M parameters and 722 FPS, offering an exceptional balance between robustness, accuracy, and efficiency.