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Sparse Adversarial Perturbations for Videos
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored. Compared with images, attacking a video needs to consider not only spatial cues but also temporal cues. Moreover, to improve the imperceptibility as well as reduce the computation cost, perturbations should be added on as few frames as possible, i.e., adversarial perturbations are temporally sparse. This further motivates the propagation of
doi:10.1609/aaai.v33i01.33018973
fatcat:cbrwfn5c35cspp4f4rw4xpbzei