Machine learning has seen a remarkable rate of adoption in recent years across a broad spectrum of industries and applications. Many applications of machine learning techniques are adversarial in nature, insofar as the goal is to distinguish instances which are bad'' from those which aregood''. Indeed, adversarial use goes well beyond this simple classification example: forensic analysis of malware which incorporates clustering, anomaly detection, and even vision systems in autonomous vehicles could all potentially be subject to attacks. In response to these concerns, there is an emerging literature on adversarial machine learning, which spans both the analysis of vulnerabilities in machine learning algorithms, and algorithmic techniques which yield more robust learning.