让检测器只关注生成痕迹，忽略真实图像特征。
Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection
- 训练时强制检测器忽略真实图像的压缩等伪特征
- 在多种后处理下仍保持高准确率，泛化能力更强
- 特别擅长识别修复过的真人照片，适合真实场景
检测人工智能生成图像是一项关键但具挑战性的任务。现有检测器常依赖压缩伪影等虚假模式做出判断，这些模式与真实数据分布相关联，难以区分真实生成痕迹。本文提出Stay Positive算法，主张仅当图像包含生成模型引入的特徵时才判为假。该方法通过约束检测器聚焦于生成性伪迹，忽略与真实图像相关的特征。实验表明，采用该方法训练的检测器对虚假相关性不敏感，泛化性能和抗后处理能力显著提升。此外，相比依赖真实图像特征的检测器，专注生成伪迹的检测器更擅长发现经过修补的真实图像。
原文摘要 · Abstract (English)
Detecting AI generated images is a challenging yet essential task. A primary difficulty arises from the detectors tendency to rely on spurious patterns, such as compression artifacts, which can influence its decisions. These issues often stem from specific patterns that the detector associates with the real data distribution, making it difficult to isolate the actual generative traces. We argue that an image should be classified as fake if and only if it contains artifacts introduced by the generative model. Based on this premise, we propose Stay Positive, an algorithm designed to constrain the detectors focus to generative artifacts while disregarding those associated with real data. Experimental results demonstrate that detectors trained with Stay Positive exhibit reduced susceptibility to spurious correlations, leading to improved generalization and robustness to post processing. Additionally, unlike detectors that associate artifacts with real images, those that focus purely on fake artifacts are better at detecting inpainted real images.
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