[2020-03-13] Prof. Shang-Tse Chen, National Taiwan University, "AI-infused Security: Robust Defense by Bridging Theory and Practice"(English Speech)

Poster:Post date:2020-03-03
Title: AI-infused Security: Robust Defense by Bridging Theory and Practice
Date: 2020-03-13 2:20pm-3:30pm
Location: R103, CSIE
Speaker: Prof. Shang-Tse Chen, National Taiwan University

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 successful deployment of AI in security settings. (1) Discovering real-world vulnerabilities of deep neural networks and the countermeasures to mitigate such 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, and then show how to use this concept to design efficient learning algorithms with strong theoretical guarantees for distributed and noisy data. 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 received his B.S. degree in Computer Science and Information Engineering from National Taiwan University (NTU CSIE) in 2010 and his Ph.D. degree in Computer Science from Georgia Tech in 2019. In 2020, Dr. Chen joins NTU CSIE as an Assistant Professor. He works in the intersection of applied and theoretical machine learning, with a strong application focus on cybersecurity. His research has led to patented 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 PhD Fellowship (2018). His recent research interests include adversarial ML and various aspects of security, privacy, and fairness of ML models.
Last modification time:2020-03-10 PM 3:11

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