Point cloud is an important 3D data representation widely used in many essential applications. Leveraging deep neural networks, recent works have shown great success in processing 3D point clouds. However, those deep neural networks are vulnerable to various 3D adversarial attacks, which can be summarized as two primary types: point perturbation that affects local point distribution, and surface distortion that causes dramatic changes in geometry. In this paper, we propose a novel 3D adversarial point cloud defense method leveraging implicit function based restoration (IF-Defense) to address both the aforementioned attacks. It is composed of two steps: 1) it predicts an implicit function that captures the clean shape through a surface recovery module, and 2) restores a clean and complete point cloud via minimizing the difference between the attacked point cloud and the predicted implicit function under geometry- and distribution- aware constraints. Our experimental results show that IF-Defense achieves the state-of-the-art defense performance against all existing adversarial attacks on PointNet, PointNet++, DGCNN and PointConv. Comparing with previous methods, IF-Defense presents 20.02% improvement in classification accuracy against salient point dropping attack and 16.29% against LG-GAN attack on PointNet.
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