Adversarial Robustness and Forensics in Deep Neural Networks

Seminar Series on Security & Privacy in Machine Learning (SPML)
Jun 21, 2022, 1:00 pm2:00 pm



Event Description

Speaker:  Ben Y. Zhao
Title:  Adversarial Robustness and Forensics in Deep Neural Networks
Day:  June 21, 2022
Time:  1:00 pm
Room:  Zoom Only
Host:  Vikash Sehwag & Prateek Mittal

Abstract: Despite their tangible impact on a wide range of real world applications, deep neural networks are known to be vulnerable to numerous attacks, including inference time attacks based on adversarial perturbations, as well as training time attacks such as backdoors. The security community has done extensive work to explore both attacks and defenses, only to produce a seemingly endless cat-and-mouse game. 

In this talk, I will talk about some of our recent work into adversarial robustness for DNNs, with a focus on ML digital forensics. I start by summarizing some of our recent projects at UChicago SAND Lab covering both sides of the attack/defense struggle, including honeypot defenses (CCS 2020) and physical domain poison attacks (CVPR 2021). Our experiences in these projects motivated us to seek a broader, more realistic view towards adversarial robustness, beyond the current static, binary views of attack and defense. Like real world security systems, we take a pragmatic view that given sufficient incentive and resources, attackers will eventually succeed in compromising DNN systems. Just as in traditional security realms, digital forensics tools can serve dual purposes: identifying the sources of the compromise so that they can be mitigated, while also providing a strong deterrent against future attackers. I will present results from our first paper in this space (Usenix Security 2022), specifically addressing forensics for poisoning attacks against DNNs, and show how we can trace back corrupted models to specific subsets of training data responsible for the corruption. Our approach builds up on ideas from model unlearning, and succeeds with high precision/recall for both dirty- and clean-label attacks.

Bio: Ben Zhao is Neubauer Professor of Computer Science at University of Chicago. Prior to joining UChicago, he held the position of Professor of Computer Science at UC Santa Barbara. He completed his Ph.D. at U.C. Berkeley (2004), and B.S. from Yale (1997). He is an ACM Fellow, and a recipient of the NSF CAREER award, MIT Technology Review's TR-35 Award (Young Innovators Under 35), ComputerWorld Magazine's Top 40 Technology Innovators award, IEEE ITC Early Career Award, and Google Faculty awards. His work has been covered by media outlets such as New York Times, Boston Globe, LA Times, MIT Tech Review, Wall Street Journal, Forbes, Fortune, CNBC, MSNBC, New Scientist, and Slashdot. He has published extensively in areas of security and privacy, machine learning, networking, and HCI. He served as TPC (co)chair for the World Wide Web conference (WWW 2016) and ACM Internet Measurement Conference (IMC 2018). He also serves on the steering committee for HotNets, and was general co-chair for HotNets 2020.

Electrical & Computer Engineering