Keywords:  AI and Machine Learning,

2017, 2018, 2019, 2020

Secure Machine Learning

David Wagner, Professor, Department of Electrical Engineering and Computer Science, UC Berkeley

We will study how to harden machine learning classifiers against adversarial attack. We will explore general mechanisms for making deep-learning classifiers more robust against attack, with a special focus on security for autonomous vehicles. Current schemes fail badly in the presence of an attacker who is trying to fool or manipulate the model, so there is a need for better defenses. We will study three specific approaches for defending machine learning: generative models, checking internal consistency, and making improvements to adversarial training.