ICML 2018oral706 citations
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, Aaron Oord
Abstract
This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. We motivate
BibTeX
@InProceedings{pmlr-v80-uesato18a,
title = {Adversarial Risk and the Dangers of Evaluating Against Weak Attacks},
author = {Uesato, Jonathan and O'Donoghue, Brendan and Kohli, Pushmeet and van den Oord, Aaron},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {5025--5034},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/uesato18a/uesato18a.pdf},
url = {https://proceedings.mlr.press/v80/uesato18a.html},
abstract = {This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. We motivate