A Theoretical Foundation and Practical Platform for Adversarial Machine Learning

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The recent impressive success of machine learning models has made them a promising candidate solution for virtually every application that requires extensive data analysis. With such bright promise comes the great scrutiny: these models are surprisingly non-robust against adversarial attacks. Many empirical studies have been performed, sometimes with seemingly contradicting conclusions. In this project, we aim to build a common theoretical foundation and a practical platform for performing research in adversarial machine learning. We provide an axiomatic approach to rigorously define, compute, and optimize robustness, along with other more conventional metrics, and we develop a platform to allow users to experiment with different notions of robustness and with different trade-offs among accuracy, robustness and efficiency. Our work will greatly clarify and streamline existing empirical work in a much needed unified and rigorous framework.