AI 看照片时，其实在偷看相机型号
你以为 AI 识别图片靠的是内容，其实它偷偷在像素里读相机型号、修图痕迹这些「隐形水印」，而且越大的模型越爱这么干。研究者发现，预训练数据里图片内容和相机型号天然相关，模型就把这种低级信号当成了捷径，导致换台相机拍同一场景，识别就崩。好消息是，他们找到了在训练前后都能削弱这种依赖的方法，顺带还解释了为什么有些模型特别擅长识别 AI 生成的图。
📄 原文摘要(英文)
Deep vision models exploit shortcuts, relying on cues that correlate with supervision signals. Prior work has focused on visible biases, such as object-background or texture correlations. We identify a different source of shortcut learning: invisible metadata traces embedded at the pixel level, for metadata such as image processing and photo acquisition. We hypothesize that large-scale semantic supervision, whether through categorical labels (ImageNet) or billion-scale captions (LAION), naturally induces metadata-semantics correlations during pretraining, leading models to convert low-level signals into predictive features. By introducing controlled metadata-semantics correlations, we show that stronger ones produce systematically higher sensitivity to metadata traces and larger performance degradation under metadata distribution shifts. We further explore mitigation strategies applied during and after pretraining that reduce sensitivity not only to targeted metadata but also to unseen ones, without sacrificing performance on downstream tasks. Metadata sensitivity also has a positive side: it partly explains the strong generated-image detection ability of some encoders, while its mitigation can improve out-of-distribution generalization. Code: https://github.com/ryan-caesar-ramos/visual-encoder-traces