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arXiv · 2601.22778

Color Matters: Demosaicing-Guided Color Correlation Training for Generalizable AI-Generated Image Detection

Nan Zhong·Yiran Xu·Mian Zou
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Abstract

As realistic AI-generated images threaten digital authenticity, we address the generalization failure of generative artifact-based detectors by exploiting the intrinsic properties of the camera imaging pipeline. Concretely, we investigate color correlations induced by the color filter array (CFA) and demosaicing, and propose a Demosaicing-guided Color Correlation Training (DCCT) framework for AI-generated image detection. By simulating the CFA sampling pattern, we decompose each color image into a single-channel input (as the condition) and the remaining two channels as the ground-truth targets (for prediction). A self-supervised U-Net is trained to model the conditional distribution of the missing channels from the given one, parameterized via a mixture of logistic functions. Our theoretical analysis reveals that DCCT targets a provable distributional difference in color-correlation features between photographic and AI-generated images. By leveraging these distinct features to construct a binary classifier, DCCT achieves state-of-the-art generalization and robustness, significantly outperforming prior methods across over 20 unseen generators.

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Nan Zhong, Yiran Xu, Mian Zou. 2026-01-30. Color Matters: Demosaicing-Guided Color Correlation Training for Generalizable AI-Generated Image Detection. https://arxiv.org/abs/2601.22778

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