Paper ID: 2202.07201

Holistic Adversarial Robustness of Deep Learning Models

Pin-Yu Chen, Sijia Liu

Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning-based technology, the potential risks associated with model development and deployment can be amplified and become dreadful vulnerabilities. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models, including attacks, defenses, verification, and novel applications.

Submitted: Feb 15, 2022