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Understanding Real-world Threats to Deep Learning Models in Android Apps. (arXiv:2209.09577v1 [cs.CR])
Sept. 21, 2022, 1:20 a.m. | Zizhuang Deng, Kai Chen, Guozhu Meng, Xiaodong Zhang, Ke Xu, Yao Cheng
cs.CR updates on arXiv.org arxiv.org
Famous for its superior performance, deep learning (DL) has been popularly
used within many applications, which also at the same time attracts various
threats to the models. One primary threat is from adversarial attacks.
Researchers have intensively studied this threat for several years and proposed
dozens of approaches to create adversarial examples (AEs). But most of the
approaches are only evaluated on limited models and datasets (e.g., MNIST,
CIFAR-10). Thus, the effectiveness of attacking real-world DL models is not
quite …
More from arxiv.org / cs.CR updates on arXiv.org
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