Wang, H orcid.org/0000-0002-2281-5679, He, F, Peng, Z et al. (4 more authors) (2021) Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20-25 Jun 2021, Nashville, TN, USA. IEEE , pp. 14651-14660. ISBN 978-1-6654-4510-8
Abstract
Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end, we propose a new method to attack action recognizers which rely on the 3D skeletal motion. Our method involves an innovative perceptual loss which ensures the imperceptibility of the attack. Empirical studies demonstrate that our method is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Our method shows that adversarial attack on 3D skeletal motions, one type of time-series data, is significantly different from traditional adversarial attack problems. Its success raises serious concern on the robustness of action recognizers and provides insights on potential improvements.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R031193/1 EU - European Union 899739 |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Mar 2021 18:18 |
Last Modified: | 16 Oct 2023 16:07 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CVPR46437.2021.01442 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171784 |