Beyond Semantic Features: Pixel-level Mapping for Generalized AI-Generated Image Detection

The rapid evolution of generative technologies necessitates reliable methods for detecting AI-generated images. A critical limitation of current detectors is their failure to generalize to images from unseen generative models, as they often overfit to source-specific semantic cues rather than learning universal generative artifacts. To overcome this, we introduce a simple yet remarkably effective pixel-level mapping pre-processing step to disrupt the pixel value distribution of images and break the fragile, non-essential semantic patterns that detectors commonly exploit as shortcuts. This forces the detector to focus on more fundamental and generalizable high-frequency traces inherent to the image generation process. Through comprehensive experiments on GAN and diffusion-based generators, we show that our approach significantly boosts the cross-generator performance of state-of-the-art detectors. Extensive analysis further verifies our hypothesis that the disruption of semantic cues is the key to generalization.

Authors

• Chenming Zhou Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China
• Jiaan Wang School of Computer Science and Technology, Soochow University, Suzhou, China
• Yu Li International Digital Economy Academy (IDEA)
• Lei Li Cleveland State University
• Juan Cao Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Sheng Tang

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