Transferable Adversarial Attack on 3D Object Tracking in Point Cloud

Conference · Mon Jan 09 04:00:00 EST 2023

DOI:https://doi.org/10.1007/978-3-031-27818-1_37· OSTI ID:1993156

Lin, Yuewei; Liu, Xiaoqiong; Lin, Yuewei; Yang, Qing; Fan, Heng

3D point cloud object tracking has recently witnessed considerable progress relying on deep learning. Such progress, however, mainly focuses on improving tracking accuracy. The risk, especially considering that deep neural network is vulnerable to adversarial perturbations, of a tracker being attacked is often neglected and rarely explored. In order to attract attentions to this potential risk and facilitate the study of robustness in point cloud tracking, we introduce a novel transferable attack network (TAN) to deceive 3D point cloud tracking. Specifically, TAN consists of a 3D adversarial generator, which is trained with a carefully designed multi-fold drift (MFD) loss. The MFD loss considers three common grounds, including classification, intermediate feature and angle drifts, across different 3D point cloud tracking frameworks for perturbation generation, leading to high transferability of TAN for attack. In our extensive experiments, we demonstrate the proposed TAN is able to not only drastically degrade the victim 3D point cloud tracker, \ie, P2B, but also effectively deceive other unseen state-of-the-art approaches such as BAT and M^2Track, posing a new threat to 3D point cloud tracking.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
• DOE Contract Number: SC0012704
• OSTI ID: 1993156
• Report Number(s): BNL-224637-2023-CPPJ
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING