  Despite remarkable performance across a broad range of tasks, neural networks
have been shown to be vulnerable to adversarial attacks. Many works focus on
adversarial attacks and defenses on 2D images, but few focus on 3D point
clouds. In this paper, our goal is to enhance the adversarial robustness of
PointNet, which is one of the most widely used models for 3D point clouds. We
apply the fast gradient sign attack method (FGSM) on 3D point clouds and find
that FGSM can be used to generate not only adversarial images but also
adversarial point clouds. To minimize the vulnerability of PointNet to
adversarial attacks, we propose Defense-PointNet. We compare our model with two
baseline approaches and show that Defense-PointNet significantly improves the
robustness of the network against adversarial samples.
