• Corpus ID: 244773413

# $\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training

@inproceedings{Dolatabadi2021ell\_inftyRobustnessAB,
title={\$\ell\_\infty\$-Robustness and Beyond: Unleashing Efficient Adversarial Training},
author={Hadi M. Dolatabadi and Sarah Monazam Erfani and Christopher Leckie},
year={2021}
}
• Published 1 December 2021
• Computer Science
, Abstract. Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output. Adversarial training is one of the most effective approaches in training robust models against such attacks. However, it is much slower than vanilla training of neural networks since it needs to construct adversarial examples for the entire training data at every iteration, hampering its effectiveness. Recently, Fast Adversarial Training…

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