Web: http://arxiv.org/abs/2209.06300

Sept. 15, 2022, 1:20 a.m. | William Hackett, Stefan Trawicki, Zhengxin Yu, Neeraj Suri, Peter Garraghan

cs.CR updates on arXiv.org arxiv.org

Deep Learning (DL) models increasingly power a diversity of applications.
Unfortunately, this pervasiveness also makes them attractive targets for
extraction attacks which can steal the architecture, parameters, and
hyper-parameters of a targeted DL model. Existing extraction attack studies
have observed varying levels of attack success for different DL models and
datasets, yet the underlying cause(s) behind their susceptibility often remain
unclear. Ascertaining such root-cause weaknesses would help facilitate secure
DL systems, though this requires studying extraction attacks in a wide …

adversarial attack deep learning framework

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