Oct. 5, 2022, 1:20 a.m. | Xinyue Shen, Xinlei He, Zheng Li, Yun Shen, Michael Backes, Yang Zhang

cs.CR updates on arXiv.org arxiv.org

Masked image modeling (MIM) revolutionizes self-supervised learning (SSL) for
image pre-training. In contrast to previous dominating self-supervised methods,
i.e., contrastive learning, MIM attains state-of-the-art performance by masking
and reconstructing random patches of the input image. However, the associated
security and privacy risks of this novel generative method are unexplored. In
this paper, we perform the first security risk quantification of MIM through
the lens of backdoor attacks. Different from previous work, we are the first to
systematically threat modeling on …

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