利用相机采样特性提升AI图像检测泛化能力
Color Matters: Demosaicing-Guided Color Correlation Training for Generalizable AI-Generated Image Detection
- 通过模拟传感器采样模式，让模型预测缺失颜色通道
- 在20+未见过的生成器上表现超越现有方法
- 适合需要跨模型检测AI生成图像的研究者
随着逼真的AI生成图像威胁数字真实性，本文针对基于生成伪影的检测器泛化能力不足的问题，利用相机成像流程的固有特性。具体而言，研究了由色彩滤镜阵列（CFA）和去马赛克过程引发的颜色相关性，提出一种去马赛克引导的颜色相关性训练（DCCT）框架用于检测AI生成图像。通过模拟CFA采样模式，将每张彩色图像分解为单通道输入（作为条件）和另外两通道作为真实目标（用于预测）。采用自监督U-Net模型，基于混合逻辑函数参数化缺失通道的条件分布。理论分析表明，DCCT可捕捉摄影图像与AI生成图像在颜色相关性特征上的可证明分布差异。利用这些差异特征构建二分类器，DCCT在跨生成器场景下达到当前最优的泛化与鲁棒性，在超过20个未见过的生成器上显著优于先前方法。
原文摘要 · Abstract (English)
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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