Classification has been the focal point of research on adversarial attacks,
but only a few works investigate methods suited to denser prediction tasks,
such as semantic segmentation. The methods proposed in these works do not
accurately solve the adversarial segmentation problem and, therefore,
overestimate the size of the perturbations required to fool models. Here, we
propose a white-box attack for these models based on a proximal splitting to
produce adversarial perturbations with much smaller $\ell_\infty$ norms. Our
attack can handle large numbers of constraints within a nonconvex minimization
framework via an Augmented Lagrangian approach, coupled with adaptive
constraint scaling and masking strategies. We demonstrate that our attack
significantly outperforms previously proposed ones, as well as classification
attacks that we adapted for segmentation, providing a first comprehensive
benchmark for this dense task.

该论文提出了一种基于近端分裂的白盒攻击方法，通过自适应约束缩放和掩蔽策略，在非凸规划框架内处理大量约束，有效地解决了对分割模型的敌对攻击问题。该攻击方法明显优于以前提出的方法，为此密集任务提供了首个全面的基准测试。