Adversarial Machine Learning and Beyond

Adversarial Machine Learning and Beyond

Abstract

Despite their great success and popularity, deep neural networks are widely known to be vulnerable to adversarial examples, i.e. small human imperceptible perturbations fooling a target model. This intriguing phenomenon has inspired numerous techniques for attack and defense. More strikingly, a single perturbation has been found to fool a model for most images. In this talk, we will give an introduction to adversarial attack and defense as well as the most recent progress on universal adversarial perturbations. Beyond the adversarial use, we also show that small imperceptible perturbations can be utilized to hide useful information for steganography, watermarking, and light field messaging.

Date
Nov 26, 2020 1:00 PM
Event
Philipp Benz
Philipp Benz
Ph.D. Candidate @ Robotics and Computer Vision Lab, KAIST

My research interest is in Deep Learning with a focus on robustness and security.