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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an l1l_1 norm. We demonstrate that achieving robustness against l1l_1-bounded perturbations is more challenging than in the l2l_2 ...
2023

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