Breaking Transferability of Adversarial Samples with Randomness Breaking Transferability of Adversarial Samples with Randomness
Paper summary Zhou et al. study transferability of adversarial examples against ensembles of randomly perturbed networks. Specifically, they consider randomly perturbing the weights using Gaussian additive noise. Using an ensemble of these perturbed networks, the authors show that transferability of adversarial examples decreases significantly. However, the authors do not consider adapting their attack to this defense scenario. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
arxiv.org
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Breaking Transferability of Adversarial Samples with Randomness
Zhou, Yan and Kantarcioglu, Murat and Xi, Bowei
arXiv e-Print archive - 2018 via Local Bibsonomy
Keywords: dblp


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Summary by David Stutz 1 week ago
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