Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training

ICLR 2018 Jan Hendrik Metzen

Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs... (read more)

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