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.

翻译：随着逼真的AI生成图像威胁数字真实性，我们通过利用相机成像流程的内在特性来解决基于生成伪影的检测器泛化失败的问题。具体而言，我们研究了由彩色滤波阵列（CFA）和去马赛克过程诱导的颜色相关性，并提出了一种用于AI生成图像检测的去马赛克引导颜色相关性训练（DCCT）框架。通过模拟CFA采样模式，我们将每个彩色图像分解为单通道输入（作为条件）和剩余两个通道作为真实目标（用于预测）。训练一个自监督U-Net模型，通过逻辑函数混合分布参数化，以建模从给定通道预测缺失通道的条件分布。我们的理论分析表明，DCCT针对摄影图像与AI生成图像之间颜色相关性特征的可证明分布差异。通过利用这些独特特征构建二元分类器，DCCT实现了最先进的泛化能力和鲁棒性，在超过20种未见生成器上显著优于现有方法。