To tackle the susceptibility of deep neural networks to examples, the
adversarial training has been proposed which provides a notion of robust
through an inner maximization problem presenting the first-order embedded
within the outer minimization of the training loss. To generalize the
adversarial robustness over different perturbation types, the adversarial
training method has been augmented with the improved inner maximization
presenting a union of multiple perturbations e.g., various $ell_p$
norm-bounded perturbations.

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