Most adversarial attacks on point clouds perturb a large number of points, causing widespread geometric changes and limiting applicability in real-world scenarios. While recent works explore sparse attacks by modifying only a few points, such approaches often struggle to maintain effectiveness due to the limited influence of individual perturbations. In this paper, we propose SCP, a sparse and cooperative perturbation framework that selects and leverages a compact subset of points whose joint perturbations produce amplified adversarial effects. Specifically, SCP identifies the subset where the misclassification loss is locally convex with respect to their joint perturbations, determined by checking the positivedefiniteness of the corresponding Hessian block. The selected subset is then optimized to generate high-impact adversarial examples with minimal modifications. Extensive experiments show that SCP achieves 100% attack success rates, surpassing state-of-the-art sparse attacks, and delivers superior imperceptibility to dense attacks with far fewer modifications.

翻译：大多数针对点云的对抗攻击会扰动大量点，导致广泛的几何变化，并限制了其在现实场景中的适用性。尽管近期研究探索了仅修改少数点的稀疏攻击，但由于单个扰动的影响有限，此类方法往往难以保持攻击效果。本文提出SCP，一种稀疏协同扰动框架，该框架选取并利用一个紧凑的点子集，通过其联合扰动产生放大的对抗效应。具体而言，SCP通过检查相应Hessian矩阵块的正定性，识别出分类损失相对于其联合扰动局部凸的子集。随后对选定的子集进行优化，以生成具有最小修改的高影响力对抗样本。大量实验表明，SCP实现了100%的攻击成功率，超越了当前最先进的稀疏攻击方法，并以远少于密集攻击的修改量，提供了更优的不可感知性。