research-article

Towards Decision-based Sparse Attacks on Video Recognition

Authors:

Wenqiang ZhangAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 1443 - 1454

https://doi.org/10.1145/3581783.3611828

Published: 27 October 2023 Publication History

Abstract

Recent studies indicate that sparse attacks threaten the security of deep learning models, which modify only a small set of pixels in the input based on the l0 norm constraint. While existing research has primarily focused on sparse attacks against image models, there is a notable gap in evaluating the robustness of video recognition models. To bridge this gap, we are the first to study sparse video attacks and propose an attack framework named V-DSA in the most challenging decision-based setting, in which threat models only return the predicted hard label. Specifically, V-DSA comprises two modules: a Cross-Modal Generator (CMG) for query-free transfer attacks on each frame and an Optical flow Grouping Evolution algorithm (OGE) for query-efficient spatial-temporal attacks. CMG passes each frame to generate the transfer video as the starting point of the attack based on the feature similarity between image classification and video recognition models. OGE first initializes populations based on transfer video and then leverages optical flow to establish the temporal connection of the perturbed pixels in each frame, which can reduce the parameter space and break the temporal relationship between frames specifically. Finally, OGE complements the above optical flow modeling by grouping evolution which can realize the coarse-to-fine attack to avoid falling into the local optimum. In addition, OGE makes the perturbation with temporal coherence while balancing the number of perturbed pixels per frame, further increasing the imperceptibility of the attack. Extensive experiments demonstrate that V-DSA achieves state-of-the-art performance in terms of both threat effectiveness and imperceptibility. We hope V-DSA can provide valuable insights into the security of video recognition systems.

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Cited By

Pan YHuang JChen ZZhao WWang Z(2024)SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10688258(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10688258
Grosse KAlahi A(2024)A qualitative AI security risk assessment of autonomous vehiclesTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104797169(104797)Online publication date: Dec-2024
https://doi.org/10.1016/j.trc.2024.104797

Index Terms

Towards Decision-based Sparse Attacks on Video Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 ACM.

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Published: 27 October 2023

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National Natural Science Foundation of China
Scientific and Technological Innovation Action Plan of Shanghai Science and Technology Committee
National Key R&D Program of China

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Pan YHuang JChen ZZhao WWang Z(2024)SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10688258(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10688258
Grosse KAlahi A(2024)A qualitative AI security risk assessment of autonomous vehiclesTransportation Research Part C: Emerging Technologies10.1016/j.trc.2024.104797169(104797)Online publication date: Dec-2024
https://doi.org/10.1016/j.trc.2024.104797

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