Towards Resilient Autonomous Cyber-Physical Systems against Adversarial Examples

Speaker: Dr. Qun SONG
City University of Hong Kong

Title: Towards Resilient Autonomous Cyber-Physical Systems against Adversarial Examples

Date: Monday, 9 March 2026

Time: 4:00pm to 5:00pm

Venue: Lecture Theater F
(Leung Yat Sing Lecture Theater), near lift 25/26, HKUST

Abstract:

Deep learning is shown susceptible to adversarial examples, which are crafted inputs aiming to cause wrong classification outputs for deep models by adding minute perturbations on the clean inputs. Thus, deploying deep learning models on safety-critical cyber-physical systems without incorporating effective countermeasures against adversarial examples raises security concerns. This talk is about the studies on the threat and countermeasures for the adversarial example attack as an ongoing concern for the safety-critical autonomous cyber-physical systems. This talk will introduce the dynamic ensemble-based defenses designed under the strategy of moving target defense that effectively counteract the adaptive adversarial example adversary for embedded deep visual sensing.

Biography:

Qun Song is currently an Assistant Professor in the Department of Electrical Engineering of City University of Hong Kong. Before that, she was an Assistant Professor in the Information Systems Technology and Design (ISTD) Pillar of Singapore University of Technology and Design (SUTD) from 2024 to 2025 and an Assistant Professor in the Embedded Systems Group of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at Delft University of Technology, the Netherlands from 2022 to 2024. She received Ph.D. from Nanyang Technological University, Singapore and B.Eng. from Nankai University, China. Her research interests include Artificial Intelligence of Things (AIoT), Cyber-physical system (CPS) robustness and resilience, and autonomous driving security and safety. She is the recipient of the 2023 MobiCom Best Community Contribution Award, the 2022 SenSys Best Paper Award Finalist, and the 2021 IPSN Best Artifact Award Runner-up.