Publication
AAAI 2023
Conference paper

Holistic Adversarial Robustness of Deep Learning Models

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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.

Date

27 Jun 2023

Publication

AAAI 2023

Authors

Topics

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