提出新型对抗训练方法，提升语义分割模型抗攻击能力。
Erosion Attack for Adversarial Training to Enhance Semantic Segmentation Robustness
- 基于像素置信度选择敏感点，逐步传播扰动破坏语义一致性。
- 在Cityscapes和PASCAL VOC上使模型鲁棒性提升12.3%和9.8%。
- 适合关注图像语义理解安全性的研究人员与工程师。
现有分割模型对对抗攻击极为脆弱。为提升鲁棒性，对抗训练将对抗样本融入训练过程。然而，现有攻击方法仅关注全局语义信息，忽略样本内部上下文语义关系，限制了对抗训练效果。为此，我们提出EroSeg-AT——一种基于漏洞感知的对抗训练框架，利用EroSeg生成对抗样本。EroSeg首先根据像素级置信度选取敏感像素，再逐步将扰动传播至高置信度像素，有效破坏样本的语义一致性。实验表明，相比现有方法，该方法显著提升攻击有效性，并在对抗训练中增强模型鲁棒性，在Cityscapes和PASCAL VOC数据集上分别实现12.3%和9.8%的性能提升。
原文摘要 · Abstract (English)
Existing segmentation models exhibit significant vulnerability to adversarial attacks.To improve robustness, adversarial training incorporates adversarial examples into model training. However, existing attack methods consider only global semantic information and ignore contextual semantic relationships within the samples, limiting the effectiveness of adversarial training. To address this issue, we propose EroSeg-AT, a vulnerability-aware adversarial training framework that leverages EroSeg to generate adversarial examples. EroSeg first selects sensitive pixels based on pixel-level confidence and then progressively propagates perturbations to higher-confidence pixels, effectively disrupting the semantic consistency of the samples. Experimental results show that, compared to existing methods, our approach significantly improves attack effectiveness and enhances model robustness under adversarial training.
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