Adversarial Geometric Attacks for 3D Point Cloud Object Tracking
Yao, Rui Zhang, Anqi Zhou, Yong Zhao, Jiaqi Liu, Bing El Saddik, Abdulmotaleb
Yao, Rui
Zhang, Anqi
Zhou, Yong
Zhao, Jiaqi
Liu, Bing
El Saddik, Abdulmotaleb
Supervisor
Department
Computer Vision
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
3D point cloud object tracking (3D PCOT) plays a vital role in applications such as autonomous driving and robotics. Adversarial attacks offer a promising approach to enhance the robustness and security of tracking models. However, existing adversarial attack methods for 3D PCOT seldom leverage the geometric structure of point clouds and often overlook the transferability of attack strategies. To address these limitations, this paper proposes an adversarial geometric attack method tailored for 3D PCOT, which includes a point perturbation attack module (non-isometric transformation) and a rotation attack module (isometric transformation). First, we introduce a curvature-aware point perturbation attack module that enhances local transformations by applying normal perturbations to critical points identified through geometric features such as curvature and entropy. Second, we design a Thompson sampling-based rotation attack module that applies subtle global rotations to the point cloud, introducing tracking errors while maintaining imperceptibility. Additionally, we design a fused loss function to iteratively optimize the point cloud within the search region, generating adversarially perturbed samples. The proposed method is evaluated on multiple 3D PCOT models and validated through black-box tracking experiments on benchmarks. For P2B, white-box attacks on KITTI reduce the success rate from 53.3% to 29.6% and precision from 68.4% to 37.1%. On NuScenes, the success rate drops from 39.0% to 27.6%, and precision from 39.9 to 26.8%. Black-box attacks show a transferability, with BAT showing a maximum 47.0% drop in success rate and 47.2% in precision on KITTI, and a maximum 22.5% and 27.0% on NuScenes.
Citation
R. Yao, A. Zhang, Y. Zhou, J. Zhao, B. Liu, and A. ElSaddik, “Adversarial Geometric Attacks for 3D Point Cloud Object Tracking,” IEEE Trans Multimedia, 2025, doi: 10.1109/TMM.2025.3557613.
Source
IEEE Transactions on Multimedia
Conference
Keywords
Adversarial attack, Visual object tracking, 3D point cloud object tracking, Geometric structure
Subjects
Source
Publisher
IEEE