The art of defense: letting networks fool the attacker

Some deep neural networks are invariant to some input transformations, such as Pointnet is permutation invariant to the input point cloud. In this paper, we demonstrated this property could be powerful in defense of gradient-based attacks. Specifically, we apply random input transformation which is invariant to the networks we want to defend. Extensive experiments demonstrate that the proposed scheme defeats various gradient-based attackers in the targeted attack setting, and breaking the attack accuracy into nearly zero. Our code is available at: {footnotesize{url{https://github.com/cuge1995/IT-Defense}}}.