While Artificial Intelligence (AI) has tremendous potential as a defense against real-world cybersecurity threats, understanding the capabilities and robustness of AI remains a fundamental challenge, especially in adversarial environments. In this talk, I address two interrelated problems that are essential to the successful deployment of AI in security settings. (1) Discovering real-world vulnerabilities of deep neural networks and countermeasures to mitigate threats. I will present ShapeShifter, the first targeted physical adversarial attack that fools state-of-the-art object detectors, and SHIELD, a real-time defense that removes adversarial noise by stochastic data compression. (2) Developing theoretically-principled methods for choosing machine models to defend against unknown future attacks. I will introduce a novel game theory concept called “diversified strategy” to help make the optimal decision with limited risk. Finally, I will share my vision on making AI more robust under different threat models, and research directions on deploying AI in security-critical and high-stakes problems.
Shang-Tse Chen is a Ph.D. Candidate in Computer Science at Georgia Tech. He works in the intersection of applied and theoretical machine learning. His research focuses on designing robust machine learning algorithms for security-critical applications. He has worked closely with industry and government partners. His research has led to patent-pending cyber threat detection technology with Symantec, open-sourced adversarial attack and defense tools with Intel, deployed fire risk prediction system with the Atlanta Fire Rescue Department. He is a recipient of the KDD Best Student Paper Runner-up Award (2016) and the IBM Ph.D. Fellowship (2018).
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