  Recently, 3D deep learning models have been shown to be susceptible to
adversarial attacks like their 2D counterparts. Most of the state-of-the-art
(SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To
reproduce these attacks in the physical scenario, a generated adversarial 3D
point cloud need to be reconstructed to mesh, which leads to a significant drop
in its adversarial effect. In this paper, we propose a strong 3D adversarial
attack named Mesh Attack to address this problem by directly performing
perturbation on mesh of a 3D object. In order to take advantage of the most
effective gradient-based attack, a differentiable sample module that
back-propagate the gradient of point cloud to mesh is introduced. To further
ensure the adversarial mesh examples without outlier and 3D printable, three
mesh losses are adopted. Extensive experiments demonstrate that the proposed
scheme outperforms SOTA 3D attacks by a significant margin. We also achieved
SOTA performance under various defenses. Our code is available at:
https://github.com/cuge1995/Mesh-Attack.
