Toward Adversarial Robustness via Semi-supervised Robust Training

16 Mar 2020Yiming LiBaoyuan WuYan FengYanbo FanYong JiangZhifeng LiShutao Xia

Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) through minimizing the adversarial risk $R_{adv}$, which encourages both the benign example $x$ and its adversarially perturbed neighborhoods within the $\ell_{p}$-ball to be predicted as the ground-truth label... (read more)

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