This talk gives a brief review of Wang group’s current research efforts at UBC, in the areas of Adversarial Deep Learning in Digital Media Security & Forensics. Deep learning has achieved state-of-the-art performances in many applications. Unfortunately, current deep learning models however could be sensitive to perturbations, giving rise to security, privacy and reliability issues in practical applications.
Under the paradigm of adversarial deep learning, as an attacker, we study potential adversarial attacks and explore novel approaches to scrutinize potential vulnerabilities of deep learning models in digital media security & forensics, by investigating three fundamental learning tasks: matching, classification and regression. Specifically, this talk presents novel attacks (both in the digital domain and in the physical domain) for several essential models belonging to the above three dominant tasks: 1) image hashing for image retrieval and authentication, as a typical matching task; 2) GAN-generated fake face imagery forensics, as a representative binary classification task; 3) multiclass image classification; 4) camera-LIDAR 3d object detection; and 5) single object tracking in videos, which is an important video surveillance model involving a combination of the matching task, the classification task and the regression task. We address security and privacy threats that arise in the above typical digital media problems and study how to fool deep learning models to make wrong decisions.
Z. Jane Wang received the B.Sc. degree from Tsinghua University in 1996 and the M.Sc. and Ph.D. degrees from the University of Connecticut in 2000 and 2002, respectively, all in electrical engineering. She has been Research Associate at the University of Maryland, College Park from 2002 to 2004. Since 2004, she has been with the ECE dept. at the University of British Columbia (UBC), Canada, and she is currently Professor. She is an IEEE Fellow, a Fellow of the Canadian Academy of Engineering (FCAE), and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada.
Her research interests are in the broad areas of statistical signal processing and machine learning, with current focuses on digital media security and biomedical data analytics. She has published 160+ journal papers and 120+ peer-reviewed conference papers. She has been key Organizing Committee Member for numerous IEEE conferences and workshops (e.g., the Finance chair of ICASSP13, the co-Technical Chair for ChinaSIP2014, GlobalSIP2017 and ICIP2021, and the co-General Chair of MMSP2018 and DSLW2021). She has been Associate Editor for the IEEE TSP, SPL, TMM, TIFS, TBME, and SPM. She is currently serving as Editor-in-Chief for the IEEE Signal Processing letters.