Machine Learning and Security: The Good, the Bad, and the Hopeful - Aleksander Madry
From Scott Jacobson on October 19th, 2018
But is that really so?
In this talk, I will discuss a major difficulty in the real-world deployment of ML: making our ML solutions be robust and secure. After briefly surveying some of the key challenges in this context, I will focus on one of the most pressing issues: the widespread vulnerability of state-of-the-art deep learning models to adversarial misclassification (aka adversarial examples). I will describe a framework that enables us to reason about this vulnerability in a principled manner as well as develop methods for alleviating the problem it poses.