Holistic Adversarial Robustness of Deep Learning Models

Authors: Pin-Yu Chen, Sijia Liu
Published: 2022-02-15 | Updated: 2023-01-05

Source: https://arxiv.org/abs/2202.07201

PDF: https://arxiv.org/pdf/2202.07201

Labels Predicted by AI

ポイズニング 防御手法 敵対的攻撃検出

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

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.