Evaluating Classifier Robustness Under Local Structure-Based Adversarial Attacks in 3D Point Clouds

Abstract

       Adversarial attacks on 3D point clouds offer different difficulties and benefits compared to the 2D image-based ones. In this work, we aim to promote the robustness of adversarial attack methods and therefore propose approaches that target subsets of critical points in point cloud with a focus on local structural rather than global topology. This approach differs from ported 2D image attack strategies, as we consider the specific properties of 3D data including irregularity, recursiveness and geometric complexity. By disturbing point locals' part from the cloud, we want to generate more effective attacks that reveal weaknesses of 3D classifiers. The approach increases the effectiveness of adversarial attacks and takes into account differences in the structures used for representation of 3D data, which requires dedicated methods to successfully manipulate its sensitivity. We also study the robustness of 3D point cloud classifiers against such targeted attacks. We analyze the vulnerabilities of classifiers when facing adversarial attacks under various attack strategies and verify strategies to make them more robust