Holistic Adversarial Robustness of Deep Learning Models. (arXiv:2202.07201v1 [cs.LG])

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.

deep learning lg