  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.
