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.
@article{arxiv.2202.07201,
title = {Holistic Adversarial Robustness of Deep Learning Models},
author = {Pin-Yu Chen and Sijia Liu},
journal= {arXiv preprint arXiv:2202.07201},
year = {2023}
}
Comments
survey paper on holistic adversarial robustness for deep learning; published at AAAI 2023 Senior Member Presentation Track