Lujo Bauer
Professor, Carnegie Mellon University Department of Electrical and Computer Engineering, Software and Societal Systems Department
Talk Title
How Adversarial ML Can Impact Real Systems
Abstract
Thousands of papers have been published about adversarial examples, which trick ML algorithms into producing the wrong output. Yet, it often isn't clear whether this threat translates to real systems, where ML is only part of the control system and the attacker is constrained on how they can access a system. In this talk I'll describe several projects that show how adversarial examples can interfere with real systems, and I'll talk about our experience with building defenses.
Bio
Lujo Bauer is a Professor of Electrical and Computer Engineering, and of Computer Science, at Carnegie Mellon University. He is also a member of CyLab, Carnegie Mellon's computer security and privacy institute. He received his B.S. in Computer Science from Yale University in 1997 and his Ph.D., also in Computer Science, from Princeton University in 2003.
Bauer's research examines many aspects of computer security and privacy, including developing high-assurance access-control systems, building systems in which usability and security co-exist, and designing practical tools for identifying software vulnerabilities. His recent work focuses on developing tools and guidance to help users stay safer online and on examining how advances in machine learning can (or might not) lead to a more secure future.
Bauer served as the program chair for the flagship computer security conferences of the IEEE (S&P 2015) and the Internet Society (NDSS 2014).