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View all- Wang ZPeng WWang LWu ZZhu PTang K(2025)EIA: Edge-Aware Imperceptible Adversarial Attacks on 3D Point CloudsMultiMedia Modeling10.1007/978-981-96-2054-8_26(348-361)Online publication date: 3-Jan-2025
SymAttack: Symmetry-aware Imperceptible Adversarial Attacks on 3D Point Clouds
Authors: 
Keke Tang, Zhensu Wang, Weilong Peng, Lujie Huang, Le Wang, Peican Zhu, Wenping Wang, Zhihong TianAuthors Info & Claims
Pages 3131 - 3140
Abstract
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.
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Index Terms
- SymAttack: Symmetry-aware Imperceptible Adversarial Attacks on 3D Point Clouds
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October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
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View all- Wang ZPeng WWang LWu ZZhu PTang K(2025)EIA: Edge-Aware Imperceptible Adversarial Attacks on 3D Point CloudsMultiMedia Modeling10.1007/978-981-96-2054-8_26(348-361)Online publication date: 3-Jan-2025
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