Test-time adversarial examples, training set poisoning, reward shaping, etc.: these attacks as studied in adversarial machine learning have one thing in common: the adversary literally wants to control a machine learning system. In this talk, we will develop this connection to control theory. The resulting view allows more clarity into adversarial learning, and opens up promising research directions.

X. Zhu is with the Department of Computer Science, University of Wisconsin-Madison, WI, USA. e-mail: jerryzhu@cs.wisc.edu Copyright ⃝c by the paper’s authors. Copying permitted for private and academic purposes. In: Joseph Collins, Prithviraj Dasgupta, Ranjeev Mittu (eds.): Proceedings of the AAAI Fall 2018 Symposium on Adversary-Aware Learning Techniques and Trends in Cybersecurity, Arlington, VA, USA, 18-19 October, 2018, published at http://ceur-ws.org