Understanding the vulnerability of skeleton-based Human Activity Recognition via black-box attack-Reference-Cited by-同舟云学术

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Understanding the vulnerability of skeleton-based Human Activity Recognition via black-box attack

Published:2024-09 Issue: Volume:153 Page:110564
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Diao Yunfeng^ORCID,Wang He^ORCID,Shao Tianjia,Yang Yongliang,Zhou Kun,Hogg David^ORCID,Wang Meng

Funder

China Scholarship Council

RCUK

Engineering and Physical Sciences Research Council

National Natural Science Foundation of China

European Commission

Publisher

Elsevier BV

Reference45 articles.

1. Intriguing properties of neural networks;Szegedy,2014

2. Defense strategies for adversarial machine learning: A survey;Bountakas;Comp. Sci. Rev.,2023

3. Adversarial attacks on time series;Karim;IEEE Trans. Pattern Anal. Mach. Intell.,2021

4. Adversarial attack on skeleton-based human action recognition;Liu;IEEE Trans. Neural Netw. Learn. Syst.,2022

5. Towards understanding the adversarial vulnerability of skeleton-based action recognition;Zheng,2020

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