论文标题
深度学习模型的整体对抗性鲁棒性
Holistic Adversarial Robustness of Deep Learning Models
论文作者
论文摘要
对抗性鲁棒性研究机器学习模型的最差表现，以确保安全性和可靠性。随着基于深度学习的技术的扩散，与模型开发和部署相关的潜在风险可以扩大并成为可怕的脆弱性。本文提供了研究主题和研究方法的基础原理的全面概述，这些研究方法是深度学习模型的对抗性鲁棒性，包括攻击，防御，验证和新颖的应用。
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.
