Abstract:While it is shown in the literature that simultaneously accurate and robust classifiers exist for common datasets, previous methods that improve the adversarial robustness of classifiers often manifest an accuracy-robustness trade-off. We build upon recent advancements in data-driven "locally biased smoothing" to develop classifiers that treat benign and adversarial test data differently. Specifically, we tailor the smoothing operation to the usage of a robust neural network as the source of robustness. We the… Show more
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