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BATT: Backdoor Attack with Transformation-based Triggers. (arXiv:2211.01806v1 [cs.CR])
Nov. 4, 2022, 1:20 a.m. | Tong Xu, Yiming Li, Yong Jiang, Shu-Tao Xia
cs.CR updates on arXiv.org arxiv.org
Deep neural networks (DNNs) are vulnerable to backdoor attacks. The backdoor
adversaries intend to maliciously control the predictions of attacked DNNs by
injecting hidden backdoors that can be activated by adversary-specified trigger
patterns during the training process. One recent research revealed that most of
the existing attacks failed in the real physical world since the trigger
contained in the digitized test samples may be different from that of the one
used for training. Accordingly, users can adopt spatial transformations as …
More from arxiv.org / cs.CR updates on arXiv.org
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