  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. Our approach enjoys three major benefits, namely, a high
success rate, a low perceptibility, and a strong ability in transferring across
different networks. These benefits mostly come from the common dummy frame
which pushes all samples towards the boundary of classification. On the other
hand, such attacks are easily to be concealed since most people would not
notice the abnormality behind the perturbed video clips. We perform experiments
on two popular datasets with six state-of-the-art video classification models,
and demonstrate the effectiveness of our approach in the scenario of universal
video attacks.
