Industrial Problems Seminar: Certified Robustness against Adversarial Attacks in Image Classification

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Fatemeh Sheikholeslami (Bosch Center for Artificial Intelligence), will be giving a talk titled "Certified Robustness against Adversarial Attacks in Image Classification."

Registration is required to access the Zoom webinar.

Abstract

Researchers have repeatedly shown that it is possible to craft adversarial attacks, i.e., small perturbations that significantly change the class label, on deep classifiers and considerably degrade their performance. This fragility can significantly hinder the deployment of deep learning-based methods in safety-critical applications. To address this, adversarial attacks can be defended against either by building robust classifiers or, by creating classifiers that can detect the presence of adversarial perturbations. I will talk about a couple of algorithms that we have developed at BCAI which provide certified defenses against different threat models.

Biography

Fatemeh Sheikholeslami received her PhD in Electrical Engineering from University of Minnesota in 2019, under the supervision of Professor Georgios Giannakis. She is currently a Machine Learning Research Scientist at Bosch Center for Artificial Intelligence with the Safe and Robust Deep Learning group.