提出DEAR方法，提升AI生成图像检测在压缩等后处理下的鲁棒性。
Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection
- 通过修复图像识别干扰特征，剔除虚假信号以聚焦真实生成痕迹。
- 在未见生成器和后处理下，检测准确率显著提升，缓解真实类偏倚问题。
- 适合关注生成内容检测鲁棒性的研究人员与安全应用开发者。
现有AI生成图像检测器虽表现优异，但其性能主要源于预测偏差：对真实图像的过度偏好严重削弱了对生成内容的敏感度，尤其在压缩、缩放等标准后处理下。我们推测这源于模型依赖于虚假特征——干扰性信号掩盖了真实的生成痕迹。为此，提出DEAR（Dissect and Prune）方法，利用修复图像识别并剔除这些干扰成分。具体而言，发现与修复区域或非修复区域强相关的特征对后处理不鲁棒。通过测量通道激活与修复掩码的对齐程度，DEAR移除两端极端特征，仅保留捕捉真实生成痕迹的特征。实验表明，该方法显著提升了对未知生成器和后处理的鲁棒性，有效缓解了预测不对称性。代码已开源。
原文摘要 · Abstract (English)
While existing AI-generated image detectors report high performance, we identify that this is largely driven by a critical prediction asymmetry: a bias toward the real class that severely limits sensitivity to generated content, especially under standard post-processing operations such as compression and resizing. We hypothesize that this stems from the model's reliance on spurious features, distracting signals that obscure true generative artifacts. To address this, we propose DEAR (Dissect and Prune), which leverages inpainted images to identify and prune these interfering components. Specifically, we find that features strongly aligned to either inpainted or non-inpainted regions are less robust to post-processing. By measuring the alignment between channel activations and inpaint masks, DEAR removes features at both extremes, retaining only those that capture genuine generative artifacts. Experimental results demonstrate that our approach significantly enhances robustness against unseen generators and post-processing, effectively mitigating the prediction asymmetry. Our code is available at https://github.com/dahyedahye/dear.
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