April 2, 2024, 7:12 p.m. | Yuxin Cao, Ziyu Zhao, Xi Xiao, Derui Wang, Minhui Xue, Jin Lu

cs.CR updates on arXiv.org arxiv.org

arXiv:2312.09935v2 Announce Type: replace-cross
Abstract: Video recognition systems are vulnerable to adversarial examples. Recent studies show that style transfer-based and patch-based unrestricted perturbations can effectively improve attack efficiency. These attacks, however, face two main challenges: 1) Adding large stylized perturbations to all pixels reduces the naturalness of the video and such perturbations can be easily detected. 2) Patch-based video attacks are not extensible to targeted attacks due to the limited search space of reinforcement learning that has been widely used …

adversarial arxiv attack attacks can challenges cs.cr cs.cv effectively efficiency examples large logo main patch recognition studies systems transfer video vulnerable

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