Scholar in residence 1 August – 31 October 2026
Fanggang Wang (Senior Member, IEEE) received the B.Eng. and Ph.D. degrees from the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China, in 2005 and 2010, respectively. He was a Post-Doctoral Fellow with the Institute of Network Coding, The Chinese University of Hong Kong, Hong Kong, from 2010 to 2012. He was a Visiting Scholar with Massachusetts Institute of Technology from 2015 to 2016 and Singapore University of Technology and Design in 2014.
He is currently a Professor with the State Key Laboratory of Advanced Rail Autonomous Operation, School of Electronic and Information Engineering, Beijing Jiaotong University. His research interests are in wireless communications, signal processing, and information theory. He served as an Editor for the IEEE COMMUNICATIONS LETTERS and a technical program committee member for several conferences.
Research Objective
This work investigates adversarial perturbation attacks against deep signal classifiers and corresponding robust defense schemes, establishing a complete analysis framework for evaluating classification model vulnerability and anti-interference resilience from both intruder and legitimate receiver perspectives. The concrete targets include two parts:
1. Design practical adversarial perturbation strategies for malicious intruders. We develop two attack modes: targeted perturbation that forces the classifier to map signals to a specified wrong category, and untargeted perturbation that causes arbitrary misclassification, under the constraint of limited perturbation power and guaranteed normal data demodulation at the receiver.
2. Propose anti-perturbation robust recognition mechanisms for legal receivers. Two scenarios are considered: defense when the receiver fully knows the perturbation generation rule, and blind defense without prior attack information.
Contact: wangfg@bjtu.edu.cn
