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Deep neural networks are known to be susceptible to adversarial noise, whichare tiny and imperceptible perturbations. Most of previous work on adversarialattack mainly focus on image models, while the vulnerability of video models isless explored. In this paper, we aim to attack video models by utilizingintrinsic movement pattern and regional relative motion among video frames. Wepropose an effective motion-excited sampler to obtain motion-aware noise prior,which we term as sparked prior. Our sparked prior underlines frame correlationsand utilizes video dynamics via relative motion. By using the sparked prior ingradient estimation, we can successfully attack a variety of videoclassification models with fewer number of queries. Extensive experimentalresults on four benchmark datasets validate the efficacy of our proposedmethod.

Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior