  The video-based action recognition task has been extensively studied in
recent years. In this paper, we study the structural vulnerability of deep
learning-based action recognition models against the adversarial attack using
the one frame attack that adds an inconspicuous perturbation to only a single
frame of a given video clip. Our analysis shows that the models are highly
vulnerable against the one frame attack due to their structural properties.
Experiments demonstrate high fooling rates and inconspicuous characteristics of
the attack. Furthermore, we show that strong universal one frame perturbations
can be obtained under various scenarios. Our work raises the serious issue of
adversarial vulnerability of the state-of-the-art action recognition models in
various perspectives.
