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    AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds

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    Name:
    1912.00461.pdf
    Size:
    2.674Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Hamdi, Abdullah cc
    Rojas, Sara
    Thabet, Ali Kassem cc
    Ghanem, Bernard cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    VCC Analytics Research Group
    Visual Computing Center (VCC)
    KAUST Grant Number
    RGC/3/3570-01-01
    Date
    2020-10-07
    Preprint Posting Date
    2019-12-01
    Online Publication Date
    2020-10-07
    Print Publication Date
    2020
    Embargo End Date
    2021-10-06
    Permanent link to this record
    http://hdl.handle.net/10754/665816
    
    Metadata
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    Abstract
    Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been extensively studied in the image domain, and has only recently been extended to 3D point clouds. In this work, we present novel data-driven adversarial attacks against 3D point cloud networks. We aim to address the following problems in current 3D point cloud adversarial attacks: they do not transfer well between different networks, and they are easy to defend against via simple statistical methods. To this extent, we develop a new point cloud attack (dubbed AdvPC) that exploits the input data distribution by adding an adversarial loss, after Auto-Encoder reconstruction, to the objective it optimizes. AdvPC leads to perturbations that are resilient against current defenses, while remaining highly transferable compared to state-of-the-art attacks. We test AdvPC using four popular point cloud networks: PointNet, PointNet++ (MSG and SSG), and DGCNN. Our proposed attack increases the attack success rate by up to 40% for those transferred to unseen networks (transferability), while maintaining a high success rate on the attacked network. AdvPC also increases the ability to break defenses by up to 38% as compared to other baselines on the ModelNet40 dataset. The code is available at https://github.com/ajhamdi/AdvPC.
    Citation
    Hamdi, A., Rojas, S., Thabet, A., & Ghanem, B. (2020). AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds. Lecture Notes in Computer Science, 241–257. doi:10.1007/978-3-030-58610-2_15
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under Award No. RGC/3/3570-01-01.
    Publisher
    Springer Nature
    Conference/Event name
    16th European Conference on Computer Vision, ECCV 2020
    ISBN
    9783030586096
    DOI
    10.1007/978-3-030-58610-2_15
    arXiv
    1912.00461
    Additional Links
    http://link.springer.com/10.1007/978-3-030-58610-2_15
    Relations
    Is Supplemented By:
    • [Software]
      Title: ajhamdi/AdvPC: AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds (ECCV 2020). Publication Date: 2019-08-26. github: ajhamdi/AdvPC Handle: 10754/668128
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-58610-2_15
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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