Holistic Adversarial Robustness of Deep Learning Models
Pin‐Yu Chen, Sijia Liu
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.
Topics & Concepts
Adversarial systemRobustness (evolution)Deep learningSoftware deploymentComputer scienceArtificial intelligenceMachine learningData scienceSoftware engineeringChemistryBiochemistryGeneAdversarial Robustness in Machine LearningAnomaly Detection Techniques and Applications