通过点级信息外推，实现多块稀疏对抗攻击的联合优化。
Sparse patches adversarial attacks via extrapolating point-wise information
- 从密集扰动中裁剪点级信息，生成多块稀疏对抗样本。
- 在多个数据集上显著超越现有方法，提升攻击成功率。
- 适用于任意数量与形状的对抗块，也支持标准稀疏攻击。
稀疏和块状对抗攻击已被证明在真实场景中具有可行性，对自动驾驶系统构成安全威胁。稀疏对抗扰动指仅影响输入中少量点的扰动，而块状攻击则限定这些稀疏扰动位于特定形状和数量的块内。然而，以往方法无法同时优化多个块的位置与扰动值。本文提出一种新方法：通过外推密集对抗扰动中的点级信息，实现多块稀疏对抗攻击的联合优化。该方法可针对任意数量与形状的块进行优化，且适用于标准稀疏攻击。实验表明，该方法在多个基准设置下显著优于当前最优水平。相关代码与实验已开源至 https://github.com/yanemcovsky/SparsePatches.git。
原文摘要 · Abstract (English)
Sparse and patch adversarial attacks were previously shown to be applicable in realistic settings and are considered a security risk to autonomous systems. Sparse adversarial perturbations constitute a setting in which the adversarial perturbations are limited to affecting a relatively small number of points in the input. Patch adversarial attacks denote the setting where the sparse attacks are limited to a given structure, i.e., sparse patches with a given shape and number. However, previous patch adversarial attacks do not simultaneously optimize multiple patches' locations and perturbations. This work suggests a novel approach for sparse patches adversarial attacks via point-wise trimming dense adversarial perturbations. Our approach enables simultaneous optimization of multiple sparse patches' locations and perturbations for any given number and shape. Moreover, our approach is also applicable for standard sparse adversarial attacks, where we show that it significantly improves the state-of-the-art over multiple extensive settings. A reference implementation of the proposed method and the reported experiments is provided at \url{https://github.com/yanemcovsky/SparsePatches.git}
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