Abstract
Existing segmentation models exhibit significant vulnerability to adversarial this http URL 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.
Abstract (translated)
现有的分割模型在对抗攻击下表现出显著的脆弱性。为了提高模型的鲁棒性，对抗训练（adversarial training）将对抗样本纳入到模型训练中。然而，现有的一些攻击方法仅考虑全局语义信息，而忽略了样本中的上下文语义关系，从而限制了对抗训练的有效性。 为了解决这一问题，我们提出了一种基于EroSeg的漏洞感知对抗训练框架（EroSeg-AT），该框架利用EroSeg生成对抗样本。EroSeg首先根据像素级别的置信度选择敏感像素，并逐步向高置信度像素传播扰动，从而有效地破坏样本的语义一致性。 实验结果表明，与现有方法相比，我们的方法在攻击效果和对抗训练下的模型鲁棒性方面均有显著提升。
URL
https://arxiv.org/abs/2601.14950
