A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES

ICLR 2020 Krishnamurthy (Dj) DvijothamJamie HayesBorja BalleZico KolterChongli QinAndras GyorgyKai XiaoSven GowalPushmeet Kohli

Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results. Although most techniques developed so far require knowledge of the architecture of the machine learning model and remain hard to scale to complex prediction pipelines, the method of randomized smoothing has been shown to overcome many of these obstacles... (read more)

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