Jun 11, 2026, cs.CVJ/K move · Enter open · S save
Ziyi Chang, Kanglei Zhou, Xiaohui Liang, Hubert P. H. Shum
Department of Computer Science, Durham University, Durham, DH1 4FL, UK · Department of Psychology and Cognitive Science, Tsinghua University, Beijing 100084, China · State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China+1
Adversarial attacks on skeletal human action recognition have received significant attention. However, existing methods typically introduce noise-like perturbations that degrade motion quality post-attack, and thereby are inherently perceptible with recent advancements in S-HAR systems. We discover that this degradation stems from the gap between empirical and true risks during the optimization process of previous adversarial attacks. To address this issue, we propose an attack where adversarial motions are obtained without compromising their motion quality. To minimize the risk gap and preserve motion quality, we propose a distribution-based adversarial attack method without introducing noise-like perturbations. To faithfully evaluate the motion quality, we propose a new metric that aligns with human perception on real-world naturalness. Experiments have been conducted on the state-of-the-art S-HAR methods across two datasets, demonstrating the superiority of our method in both the attack success rate and the post-attack motion quality through qualitative and quantitative analyses. The success of our quality-preserving attack application and distribution-based method raises serious concerns about the robustness of action recognizers, highlighting the need for further enhancements in this domain.