Previous adversarial attacks on 3D point clouds mainly focus on add perturbation to the original point cloud, but the generated adversarial point cloud example does not strictly represent a 3D object in the physical world and has lower transferability or easily defend by the simple SRS/SOR. In this paper, we present a novel adversarial attack, named Mesh Attack to address this problem. Specifically, we perform perturbation on the mesh instead of point clouds and obtain the adversarial mesh examples and point cloud examples simultaneously. To generate adversarial examples, we use a differential sample module that back-propagates the loss of point cloud classifier to the mesh vertices and a mesh loss that regularizes the mesh to be smooth. Extensive experiments demonstrated that the proposed scheme outperforms the SOTA attack methods. Our code is available at: {\footnotesize{\url{https://github.com/cuge1995/Mesh-Attack}}}.
翻译:先前对 3D 点云的对抗性攻击主要侧重于在原始点云上增加扰动,但生成的对称点云样板严格来说并不代表物理世界中的三维对象,而且具有较低的可转移性或容易由简单的 SRS/ SOR 保护。在本文中,我们展示了一种新的对抗性攻击,名为 Mesh Att,以解决这一问题。具体地说,我们在网目上而不是点云上进行扰动,同时获取对称网目云示例和点云示例。为生成对抗性实例,我们使用一个差异样本模块,将点云分类器丢失情况反馈到网状顶部,而网状的网状损失则使网状变得平滑。广泛的实验表明,拟议的计划优于SOTA攻击方法。我们的代码可以在以下查阅: hiototesify huurl{https://github.com/cout1995/Mesh-Atack}。