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

• URL: http://arxiv.org/abs/2601.22778v2
• Date: Wed, 04 Feb 2026 09:04:42 GMT
• Title: Color Matters: Demosaicing-Guided Color Correlation Training for Generalizable AI-Generated Image Detection
• Abstract summary: We propose a Demosaicing-guided Color Correlation Training framework for AI-generated image detection.<n>A self-supervised U-Net is trained to model the conditional distribution of the missing channels from the given one.<n>Our theoretical analysis reveals that DCCT targets a provable distributional difference in color-correlation features between photographic and AI-generated images.
• Score: 10.845173807400533
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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