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Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13663))

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Abstract

With the maturity of depth sensors, point clouds have received increasing attention in various applications such as autonomous driving, robotics, surveillance, etc., while deep point cloud learning models have shown to be vulnerable to adversarial attacks. Existing attack methods generally add/delete points or perform point-wise perturbation over point clouds to generate adversarial examples in the data space, which may neglect the geometric characteristics of point clouds. Instead, we propose point cloud attacks from a new perspective—Graph Spectral Domain Attack (GSDA), aiming to perturb transform coefficients in the graph spectral domain that corresponds to varying certain geometric structure. In particular, we naturally represent a point cloud over a graph, and adaptively transform the coordinates of points into the graph spectral domain via graph Fourier transform (GFT) for compact representation. We then analyze the influence of different spectral bands on the geometric structure of the point cloud, based on which we propose to perturb the GFT coefficients in a learnable manner guided by an energy constraint loss function. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT (IGFT). Experimental results demonstrate the effectiveness of the proposed GSDA in terms of both imperceptibility and attack success rates under a variety of defense strategies. The code is available at https://github.com/WoodwindHu/GSDA.

Q. Hu and D. Liu—Contributed equally to this work.

This work was supported by National Natural Science Foundation of China under Contract No. 61972009.

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Authors and Affiliations

  1. Wangxuan Institute of Computer Technology, Peking University, No. 128, Zhongguancun North Street, Beijing, China

    Qianjiang Hu, Daizong Liu & Wei Hu

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  1. Qianjiang Hu
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  2. Daizong Liu
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  3. Wei Hu
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Correspondence to Wei Hu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Hu, Q., Liu, D., Hu, W. (2022). Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13663. Springer, Cham. https://doi.org/10.1007/978-3-031-20062-5_14

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  • DOI: https://doi.org/10.1007/978-3-031-20062-5_14

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