Abstract
Robust semantic segmentation is crucial for safe autonomous driving, yet deployed models remain vulnerable to black-box adversarial attacks when target weights are unknown. Most existing approaches either craft image-wide perturbations or optimize patches for a single architecture, which limits their practicality and transferability. We introduce OmniPatch, a training framework for learning a universal adversarial patch that generalizes across images and both ViT and CNN architectures without requiring access to target model parameters.
Abstract (translated)
鲁棒的语义分割对于安全自动驾驶至关重要，但现有部署模型在目标权重未知时仍易受黑盒对抗攻击。大多数现有方法要么生成全图像扰动，要么针对单一架构优化补丁，这限制了其实用性和迁移性。我们提出OmniPatch，一种用于学习通用对抗补丁的训练框架，该补丁可跨图像泛化，并同时适用于ViT和CNN架构，且无需访问目标模型参数。
URL
https://arxiv.org/abs/2603.20777
