@inproceedings{zhai2020macer,
author = {Zhai, Runtian and Dan, Chen and He, Di and Zhang, Huan and Gong, Boqing and Ravikumar, Pradeep and Hsieh, Cho-Jui and Wang, Liwei},
title = {MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius},
booktitle = {Eighth International Conference on Learning Representations (ICLR)},
year = {2020},
month = {April},
abstract = {Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly. In this paper, we propose the MACER algorithm, which learns robust models without using adversarial training but performs better than all existing provable l2-defenses. Recent work shows that randomized smoothing can be used to provide a certified l2 radius to smoothed classifiers, and our algorithm trains provably robust smoothed classifiers via MAximizing the CErtified Radius (MACER). The attack-free characteristic makes MACER faster to train and easier to optimize. In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, ImageNet, MNIST, and SVHN. For all tasks, MACER spends less training time than state-of-the-art adversarial training algorithms, and the learned models achieve larger average certified radius.},
url = {http://approjects.co.za/?big=en-us/research/publication/macer-attack-free-and-scalable-robust-training-via-maximizing-certified-radius/},
}