PointDP: DIFFUSION-DRIVEN PURIFICATION AGAINST 3D ADVERSARIAL POINT CLOUDS

Abstract

3D Point cloud is a critical data representation in many real-world applications, such as autonomous driving, robotics, and medical imaging. Although the success of deep learning further accelerates the adoption of 3D point clouds in the physical world, deep learning is notoriously vulnerable to adversarial attacks. Various defense solutions have been proposed to build robust models against adversarial attacks. In this work, we identify that the state-of-the-art empirical defense, adversarial training, has a major limitation in 3D point cloud models due to gradient obfuscation, resulting in significant degradation of robustness against strong attacks. To bridge the gap, we propose PointDP, a purification strategy that leverages diffusion models to defend against 3D adversarial attacks. Since PointDP does not rely on predefined adversarial examples for training, it can defend against diverse threats. We extensively evaluate PointDP on six representative 3D point cloud architectures and leverage sixteen strong and adaptive attacks to demonstrate its lower-bound robustness. Our evaluation shows that PointDP achieves significantly better (i.e., 12.6%-40.3%) adversarial robustness than state-of-the-art methods under strong attacks bounded by different ℓ p norms.

1. INTRODUCTION

Point cloud data is emerging as one of the most broadly used representations in 3D computer vision. It is a versatile data format available from various sensors like LiDAR and stereo cameras and computer-aided design (CAD) models, which depict physical objects by many coordinates in the 3D space. Many deep learning-based 3D perception models have been proposed [59, 34, 43, 60, 41, 9] and thus realized several safety-critical applications (e.g., autonomous driving) [81, 46, 45] . Although deep learning models [41, 42] have exhibited performance boost on many challenging tasks, extensive studies show that they are notoriously vulnerable to adversarial attacks [5, 49, 68] , where attackers manipulate the input in an imperceptible manner, which will lead to incorrect predictions of the target model. Because of the broad applications of 3D point clouds in safety-critical fields, it is imperative to study the adversarial robustness of point cloud recognition models. The manipulation space for 2D adversarial attacks is to change pixel-level numeric values of the input images. Unlike adversarial examples in 2D applications, the flexible representation of 3D point clouds results in an arguably larger attack surface. For example, adversaries could shift and detach existing points [88] , add new points into the pristine point cloud [50], or even generate totally new point clouds [89] to launch attacks. Different strategies, including limits on the number of altered points and constraints on the maximal magnitude of shifted points [50] were proposed to make attacks less perceptible. The flexibility of 3D point cloud data formats enables diverse attacks, thus hindering a practical and universal defense design. Given the safety-critical property involved in 3D point cloud applications, various studies have been devoted to advancing the robustness of 3D point cloud recognition models. DUP-Net [90] and GvG-PointNet++ [14] pioneered to add statistical outlier removal (SOR) modules as pre-processing and in-network blocks, respectively, as mitigation strategies. More lately, Sun et al. 



[51]  broke the robustness of DUP-Net and GvG-PointNet++ by specific adaptive attacks. Adversarial training has been acknowledged as the most potent defense to deliver strong empirical robustness on PointNet,DGCNN, and PCT [50]. Meanwhile, advanced purification strategies like IF-Defense [66] and LPC [25] leverage more complex modules to cleanse the adversarial point clouds. However, given that point cloud is a sparse and unstructured data format, it motivates us to re-think that whether

