The Devil is in the GAN - Defending Deep Generative Models Against Adversarial Attacks

This course aims to teach learners how to defend Deep Generative Models (DGMs) against adversarial attacks. The learning outcomes include understanding the threat model for training-time attacks on DGMs, detecting backdoors in pre-trained DGMs, and implementing effective defense mechanisms. The course covers skills such as model inspection, brute force sampling, and basic defense strategies. The teaching method involves a presentation that includes research goals, applications of DGMs, potential attack scenarios, and defense strategies. The intended audience for this course includes individuals interested in cybersecurity, machine learning, and deep learning.