The evolution and dissemination of AI-synthesized images is occurring at an unprecedented rate. Image generators are making rapid progress in their goal of perfectly imitating natural images, which also challenges image forensics. In this work, we exploit an underexplored cue in current generative models, namely their weakness to imitate color statistics of natural images. We first show that the LPIPS loss used for training image generators is less sensitive to chrominance than to luminance, which may lead to statistical discrepancies in the colors of synthetic images. Building on this observation, we then introduce six hand-crafted color transformations and a method to learn a task-optimized color transform to statistically expose generated images. These transformations can be used in various ways. First, we define color-sensitive features at pixel-level or patch-level. A simple, interpretable classifier achieves with these features an average generalization accuracy of 93.27% and strong robustness against six types of post-processing. Second, we demonstrate that the transformations exhibit characteristic visual noise patterns in natural and synthetic image areas, which enables an intuitive visual image evaluation. Third, we demonstrate that the transforms can enhance color patterns in generated images for improved multiclass attribution.

翻译：AI合成图像的生成与传播正以前所未有的速度发展。图像生成器在完美模仿自然图像的目标上取得了快速进展，这也对图像取证领域构成了挑战。本研究发掘了当前生成模型中一个尚未被充分探索的关键短板——即它们模仿自然图像颜色统计特征的薄弱性。我们首先证明，用于训练图像生成器的LPIPS损失函数对色度的敏感度低于亮度，这可能导致合成图像在颜色统计特征上存在偏差。基于这一发现，我们提出了六种手工设计的颜色变换方法，以及一种通过任务优化学习颜色变换的技术，以从统计角度识别生成图像。这些变换方法可应用于多种场景：首先，我们定义了像素级或图像块级的颜色敏感特征。采用简单的可解释分类器时，这些特征的平均泛化准确率达93.27%，并对六类后处理操作展现出强鲁棒性。其次，我们证明这些变换在自然图像与合成图像区域会呈现特征性的视觉噪声模式，可实现直观的图像质量评估。第三，我们证实这些变换能增强生成图像中的颜色模式，从而提升多类别溯源归因的性能。