Unsupervised learning can detect unknown adversarial attacks

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. There's growing concern about new security threats that arise from machine learning models becoming an important component of many critical applications. At the top of the list of threats are adversarial attacks, data samples that have been inconspicuously modified to manipulate the behavior of the targeted machine learning model. Adversarial machine learning has become a hot area of research and the topic of talks and workshops at artificial intelligence conferences. Scientists are regularly finding new ways to attack and defend machine learning models.