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There have been many efforts in attacking image classification models withadversarial perturbations, but the same topic on video classification has notyet been thoroughly studied. This paper presents a novel idea of video-basedattack, which appends a few dummy frames (e.g., containing the texts of `thanksfor watching') to a video clip and then adds adversarial perturbations only onthese new frames. Our approach enjoys three major benefits, namely, a highsuccess rate, a low perceptibility, and a strong ability in transferring acrossdifferent networks. These benefits mostly come from the common dummy framewhich pushes all samples towards the boundary of classification. On the otherhand, such attacks are easily to be concealed since most people would notnotice the abnormality behind the perturbed video clips. We perform experimentson two popular datasets with six state-of-the-art video classification models,and demonstrate the effectiveness of our approach in the scenario of universalvideo attacks.

Appending Adversarial Frames for Universal Video Attack