@inproceedings{
tang2024symattack,
title={SymAttack: Symmetry-aware Imperceptible Adversarial Attacks on 3D Point Clouds},
author={Keke Tang and Zhensu Wang and Weilong Peng and Lujie Huang and Le Wang and Peican Zhu and Wenping Wang and Zhihong Tian},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=gP6pKHuIaP}
}
Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Despite leveraging various geometric constraints, current  adversarial attack strategies often suffer from inadequate imperceptibility. Given that adversarial perturbations tend to disrupt the inherent symmetry in objects, we recognize this disruption as the primary cause of the lack of imperceptibility in these attacks. In this paper, we introduce a novel framework, symmetry-aware imperceptible adversarial attacks on 3D point clouds (SymAttack), to address this issue. Our approach starts by identifying part- and patch-level symmetry elements,  and grouping points based on semantic  and Euclidean  distances, respectively. During  the adversarial attack iterations, we intentionally adjust the perturbation vectors on symmetric points relative to their symmetry plane. By preserving symmetry within the attack process, SymAttack significantly enhances imperceptibility. Extensive experiments validate the effectiveness of SymAttack in generating imperceptible adversarial point clouds, demonstrating its superiority over the state-of-the-art methods. Codes will be made public upon paper acceptance.
SymAttack: Symmetry-aware Imperceptible Adversarial Attacks on 3D Point Clouds
Keke Tang Zhensu Wang Weilong Peng Lujie Huang Le Wang Peican Zhu Wenping Wang Zhihong Tian