Black-box adversarial attacks on video recognition models have been explored. Considering the temporal interactions between frames, a few methods try to select some key frames, and then perform attacks on them. Unfortunately, their selecting strategy is independent with the attacking step, resulting in the limited performance. Instead, we argue the frame selection phase is closely relevant with the attacking phase. The key frames should be adjusted according to the attacking results. For that, we formulate the black-box video attacks into Reinforcement Learning (RL) framework. Specifically, the environment in RL is set as the threat model, and the agent in RL plays the role of frame selecting. By continuously querying the threat models and receiving the attacking feedback, the agent gradually adjusts its frame selection strategy and adversarial perturbations become smaller and smaller. A series of experiments demonstrate that our method can significantly reduce the adversarial perturbations with efficient query times.
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