Appending Adversarial Frames for Universal Video Attack

10 Dec 2019Zhikai ChenLingxi XieShanmin PangYong HeQi Tian

There have been many efforts in attacking image classification models with adversarial perturbations, but the same topic on video classification has not yet been thoroughly studied. This paper presents a novel idea of video-based attack, which appends a few dummy frames (e.g., containing the texts of `thanks for watching') to a video clip and then adds adversarial perturbations only on these new frames... (read more)

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