Events

Xingjun Ma: "Adversarial, Backdoor and Unlearnable"

Event Type

Lecture

Sponsor

Computer Science Department at the University of Illinois, Secure Learning Lab

Location

https://illinois.zoom.us/j/89959281193?pwd=alVReHlaS0NYVENUeExBVTJNOEttQT09

Virtual

Date

Sep 28, 2021   3:30 - 4:45 pm  

Speaker

Dr. Xingjun (Daniel) Ma, Assistant Professor, School of Information Technology at Deakin University, Australia

Contact

Madeleine Garvey

E-Mail

mgarvey@illinois.edu

Phone

217-300-6342

Views

12

Originating Calendar

Computer Science Speakers Calendar

Abstract: In this talk, I will introduce my three ICLR2021 works on 1) backdoor defense, 2) adversarial
defense, and 3) data protection, respectively. The first work explores a neural attention distillation
approach to erase backdoors from deep neural networks (DNNs). The second work reveals the
magnitude and frequency characteristics of adversarially robust activations at the intermediate 
layers of DNNs and introduces a channel-wise activation suppressing (CAS) technique to
robustify DNNs. The third work proposes a type of error-minimizing noise to fool DNNs to believe
there is nothing to learn from the training data so as to achieve an effect of “unlearnable” for the
purpose of data protection. I will share some insights into unlearnable examples, their current
limitations and the opportunities.

Bio:Dr. Xingjun (Daniel) Ma is an assistant professor in the School of Information Technology at
Deakin University and an honorary fellow at The University of Melbourne. He obtained his Ph.D.
degree in machine learning from The University of Melbourne where he also worked as a
postdoctoral research fellow for one year and a half. His research interests include adversarial
machine learning, weakly supervised learning, AI security, and data privacy. He has published
20+ works at top-tier conferences such as ICML, ICLR, CVPR, ICCV, ECCV, AAAI, and IJCAI.
These works have made substantial impacts in the machine learning community with either
theoretical contributions or new SOTA results. His work on “unlearnable examples” in 2021 was
recently featured by MIT Technology Review. He also serves as a PC/SPC member or reviewer
for a number of leading machine learning conferences and journals.