Reference-Guided Whitening and Contrast for AI-Generated Image Detection

Advances in image generation have made synthetic imagery more diverse and realistic, increasing the risks of manipulation and misinformation, yet existing methods often overfit to generator-specific artifacts or shortcuts in their training data, performing well within the training generator but degrading sharply on unseen generators. In this paper, we propose CoRe (Content-and-Real Referencing), a reference-guided approach with two components. Real-Referenced Whitening (RRW) whitens features with a covariance estimated from real images only, discounting the high-variance directions of natural variation and emphasizing the low-variance directions where transferable evidence lies. Content-Referenced Fake Contrastive (CRFC) then operates on the whitened features, contrasting each fake against content-similar reals and content-different fakes, so content cannot act as a shortcut. We instantiate CoRe on the Perception Encoder (PE), a recent vision foundation model whose features exhibit particularly favorable anisotropy for this purpose. On the recent and challenging AIGIBench and Chameleon benchmarks, our method achieves state-of-the-art mean accuracy, exceeding the strongest baseline on AIGIBench by 5.7 points, Chameleon (SD v1.4) by 7.9 points.