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.
Adversarial Attacks on Skeleton-Based Sign Language Recognition
Lect.Notes Computer
International Conference on Intelligent Robotics and Applications ; 2023 ; Hangzhou, China July 05, 2023 - July 07, 2023
2023-10-21
11 pages
Article/Chapter (Book)
Electronic Resource
English
Sign language recognition , Adversarial attacks , Robustness Computer Science , Artificial Intelligence , Software Engineering/Programming and Operating Systems , Computer Applications , Computer Communication Networks , Special Purpose and Application-Based Systems , User Interfaces and Human Computer Interaction
Adversarial Attacks on Skeleton-Based Sign Language Recognition
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