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ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles. (arXiv:2009.09560v2 [cs.CV] UPDATED)
Jan. 27, 2022, 2:10 a.m. | Xiaoyong Yuan, Leah Ding, Lan Zhang, Xiaolin Li, Dapeng Wu
cs.CV updates on arXiv.org arxiv.org
Deep neural networks (DNNs) have become the essential components for various
commercialized machine learning services, such as Machine Learning as a Service
(MLaaS). Recent studies show that machine learning services face severe privacy
threats - well-trained DNNs owned by MLaaS providers can be stolen through
public APIs, namely model stealing attacks. However, most existing works
undervalued the impact of such attacks, where a successful attack has to
acquire confidential training data or auxiliary data regarding the victim DNN.
In this …
More from arxiv.org / cs.CV updates on arXiv.org
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