PhyTrace: Tracing Physical Inconsistency in AI-Generated Images via ISP Emulation

The high realism of AI-generated images has emerged as a significant cybersecurity threat. While existing detection methods have achieved some success, most rely on fixed models that are incapable of adapting to new data or generating model updates. This paper overcomes these limitations by shifting the focus to the fundamental imaging process of real images: Image Signal Processing (ISP). Unlike real images, AI-generated images do not undergo this process, making them more susceptible to physical variations within ISP modules. By analyzing how ISP sub-modules influence the physical characteristics of imaging, we simulate the ISP mapping process to amplify the differences in physical responses between real and AI-generated images during ISP transformations. Tracing these physical differences, we propose PhyTrace, a novel training-free method for detecting AI-generated images. PhyTrace enforces physical consistency constraints within ISP, operates independently of specific datasets and generative models, and effectively detects a wide range of AI-generated images. PhyTrace reveals distinct distribution patterns of real and AI-generated images. Extensive experiments on 18 test sets demonstrate that our method outperforms prior approaches in average precision and generalization, offering a robust solution for AI-generated image detection in open-world scenarios.