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
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/).