PWAVEP: 通过频谱图小波净化3D点云中的不可察觉对抗扰动
PWAVEP: Purifying Imperceptible Adversarial Perturbations in 3D Point Clouds via Spectral Graph Wavelets
AI总结：
PWAVEP通过频谱图小波技术净化3D点云中的不可察觉对抗扰动，提升准确性和鲁棒性。
摘要：
最近在3D点云上的对抗攻击进展，特别是在实现空间不可察觉性和高攻击性能方面，给防御者带来了重大挑战。当前的防御方法仍然繁琐，通常需要侵入性的模型修改、昂贵的训练过程或辅助数据访问。为了解决这些威胁，在本文中，我们提出了一种即插即用且非侵入性的防御机制，基于对不可察觉扰动与高频谱成分之间关系的理论和经验分析。在此基础上，我们引入了一种新的净化框架，称为PWAVEP，它首先为每个点计算频谱图小波域显著性分数和局部稀疏性分数。指导这些值，PWAVEP采用分层策略，它消除最显著的点，这些点被识别为难以恢复的对抗性异常值。同时，它对一组中等显著性的点应用频谱过滤过程。这一过程利用图小波变换来衰减与目标点相关的高频系数，从而有效抑制对抗噪声。广泛的评估表明，所提出的PWAVEP在准确性和鲁棒性方面优于现有方法，推动了3D点云净化的前沿。代码和数据集可在https://github.com/a772316182/pwavep上获取。
英文摘要：
Recent progress in adversarial attacks on 3D point clouds, particularly in achieving spatial imperceptibility and high attack performance, presents significant challenges for defenders. Current defensive approaches remain cumbersome, often requiring invasive model modifications, expensive training procedures or auxiliary data access. To address these threats, in this paper, we propose a plug-and-play and non-invasive defense mechanism in the spectral domain, grounded in a theoretical and empirical analysis of the relationship between imperceptible perturbations and high-frequency spectral components. Building upon these insights, we introduce a novel purification framework, termed PWAVEP, which begins by computing a spectral graph wavelet domain saliency score and local sparsity score for each point. Guided by these values, PWAVEP adopts a hierarchical strategy, it eliminates the most salient points, which are identified as hardly recoverable adversarial outliers. Simultaneously, it applies a spectral filtering process to a broader set of moderately salient points. This process leverages a graph wavelet transform to attenuate high-frequency coefficients associated with the targeted points, thereby effectively suppressing adversarial noise. Extensive evaluations demonstrate that the proposed PWAVEP achieves superior accuracy and robustness compared to existing approaches, advancing the state-of-the-art in 3D point cloud purification. Code and datasets are available at https://github.com/a772316182/pwavep
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