Skeleton-based human action recognition technologies are increasingly used in video based applications, such as home robotics, healthcare on aging population, and surveillance. However, such models are vulnerable to adversarial attacks, raising serious concerns for their use in safety-critical applications. To develop an effective defense against attacks, it is essential to understand how such attacks mislead the pose detection models into making incorrect predictions. We present SkeletonVis, the first interactive system that visualizes how the attacks work on the models to enhance human understanding of attacks.
@article{arxiv.2101.10586,
title = {SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models},
author = {Haekyu Park and Zijie J. Wang and Nilaksh Das and Anindya S. Paul and Pruthvi Perumalla and Zhiyan Zhou and Duen Horng Chau},
journal= {arXiv preprint arXiv:2101.10586},
year = {2021}
}