Master’s Thesis Presentation • Machine Learning — Wasserstein Adversarial RobustnessExport this event to calendar

Friday, September 11, 2020 — 10:00 AM EDT

Please note: This master’s thesis presentation will be given online.

Kaiwen Wu, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Yaoliang Yu

Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to “small, imperceptible” perturbations known as adversarial attacks. While the majority of existing attacks focus on measuring perturbations under the $\ell_p$ metric, Wasserstein distance, which takes geometry in pixel space into account, has long been known to be a suitable metric for measuring image quality and has recently risen as a compelling alternative to the $\ell_p$ metric in adversarial attacks. However, constructing an effective attack under the Wasserstein metric is computationally much more challenging and calls for better optimization algorithms. 

We address this gap in two ways: (a) we develop an exact yet efficient projection operator to enable a stronger projected gradient attack; (b) we show that the Frank-Wolfe method equipped with a suitable linear minimization oracle works extremely fast under Wasserstein constraints. Our algorithms not only converge faster but also generate much stronger attacks. For instance, we decrease the accuracy of a residual network on CIFAR-10 to $3.4\%$ within a Wasserstein perturbation ball of radius $0.005$, in contrast to $65.6\%$ using the previous Wasserstein attack based on an \emph{approximate} projection operator. Furthermore, employing our stronger attacks in adversarial training significantly improves the robustness of adversarially trained models. Our algorithms are applicable to general Wasserstein constrained optimization problems in other domains beyond adversarial robustness.


To join this master’s thesis presentation virtually on Zoom, please go to https://vectorinstitute.zoom.us/j/9642349173.

Location 
Online presentation
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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