Adversarial attacks on skeleton-based sign language recognition

Yufeng Li, Meng Han*, Jiahui Yu, Changting Lin, Zhaojie Ju

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Despite the impressive performance achieved by sign language recognition systems based on skeleton information, our research has uncovered their vulnerability to malicious attacks. In response to this challenge, we present an adversarial attack specifically designed to sign language recognition models that rely on extracted human skeleton data as features. Our attack aims to assess the robustness and sensitivity of these models, and we propose adversarial training techniques to enhance their resilience. Moreover, we conduct transfer experiments using the generated adversarial samples to demonstrate the transferability of these adversarial examples across different models. Additionally, by conducting experiments on the sensitivity of sign language recognition models, we identify the optimal experimental parameter settings for achieving the most effective attacks. This research significantly contributes to future investigations into the security of sign language recognition.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings
EditorsHuayong Yang, Jun Zou, Geng Yang, Xiaoping Ouyang, Honghai Liu, Zhiyong Wang, Zhouping Yin, Lianqing Liu
Number of pages11
ISBN (Electronic)9789819964833
ISBN (Print)9789819964826
Publication statusPublished - 21 Oct 2023
Event16th International Conference on Intelligent Robotics and Applications, ICIRA 2023 - Hangzhou, China
Duration: 5 Jul 20237 Jul 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Intelligent Robotics and Applications, ICIRA 2023


  • adversarial attacks
  • robustness
  • sign language recognition

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