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Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
Jiachen Sun · Yulong Cao · Christopher B Choy · Zhiding Yu · Anima Anandkumar · Zhuoqing Morley Mao · Chaowei Xiao

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

3D point cloud data is increasingly used in safety-critical applications such as autonomous driving. Thus, the robustness of 3D deep learning models against adversarial attacks becomes a major consideration. In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds with adversarial training. Specifically, we study MLP-based (PointNet), convolution-based (DGCNN), and transformer-based (PCT) 3D architectures. Through extensive experimentation, we demonstrate that appropriate applications of self-supervision can significantly enhance the robustness in 3D point cloud recognition, achieving considerable improvements compared to the standard adversarial training baseline. Our analysis reveals that local feature learning is desirable for adversarial robustness in point clouds since it limits the adversarial propagation between the point-level input perturbations and the model's final output. This insight also explains the success of DGCNN and the jigsaw proxy task in achieving stronger 3D adversarial robustness.

Author Information

Jiachen Sun (University of Michigan)
Yulong Cao (University of Michigan)
Christopher B Choy (Stanford University)
Zhiding Yu (NVIDIA)
Anima Anandkumar (NVIDIA/Caltech)
Zhuoqing Morley Mao (University of Michigan)
Chaowei Xiao (University of Michigan, Ann Arbor)

I am Chaowei Xiao, a third year PhD student in CSE Department, University of Michigan, Ann Arbor. My advisor is Professor Mingyan Liu . I obtained my bachelor's degree in School of Software from Tsinghua University in 2015, advised by Professor Yunhao Liu, Professor Zheng Yang and Dr. Lei Yang. I was also a visiting student at UC Berkeley in 2018, advised by Professor Dawn Song and Professor Bo Li. My research interest includes adversarial machine learning.

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