AAAI 2023technical24 citations
Holistic Adversarial Robustness of Deep Learning Models
Abstract
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.
BibTeX
@article{Chen_Liu_2024, title={Holistic Adversarial Robustness of Deep Learning Models}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26797}, DOI={10.1609/aaai.v37i13.26797}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Pin-Yu and Liu, Sijia}, year={2024}, month={Jul.}, pages={15411-15420} }