Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition. (arXiv:2211.13171v1 [cs.CV])

Black-box adversarial attacks present a realistic threat to action
recognition systems. Existing black-box attacks follow either a query-based
approach where an attack is optimized by querying the target model, or a
transfer-based approach where attacks are generated using a substitute model.
While these methods can achieve decent fooling rates, the former tends to be
highly query-inefficient while the latter assumes extensive knowledge of the
black-box model's training data. In this paper, we propose a new attack on
action recognition that …

action attack box recognition