  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}}}.
