University of Minnesota
School of Physics & Astronomy

Center for Excellence in Sensing Technologies and Analytics Seminar

Tuesday, April 2nd 2019
Speaker: Mingyi Hong
Subject: Recent Advances in Adversarial Machine Learning

Recently, it has been observed that machine learning algorithms and models, especially deep neural networks, are vulnerable to adversarial examples. For example, in image classification problems, one can design algorithms to generate adversarial examples for almost every image with very small human-imperceptible perturbation. In this talk, we will give an introduction to recent advances in designing adversary examples for machine learning models. In particular, we will show that how different types of system design, and optimization methods, can be used to build powerful black-box adversarial attacks for existing machine learning models. Our focus will be given to generic algorithm design, as well as to illustrating the connections and empirical performance of different approaches.

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