Holistic Adversarial Robustness of Deep Learning Models
This is an incremental work that synthesizes existing knowledge on adversarial robustness for researchers and practitioners in machine learning.
The paper provides a comprehensive overview of research topics and foundational principles for adversarial robustness in deep learning models, addressing attacks, defenses, verification, and novel applications to tackle safety and reliability issues.
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.