Adversarial Machine Learning: Attacks, Defenses, and Open Challenges
This research addresses a significant problem for the machine learning community, particularly those working on security and robustness of AI systems, by providing a comprehensive analysis of adversarial machine learning attacks and defenses.
This study tackles the problem of vulnerabilities in AI systems, addressing evasion and poisoning attacks, and discusses the challenges of implementing robust defense solutions, without providing concrete numerical results. The study highlights open challenges in certified robustness, scalability, and real-world deployment.
Adversarial Machine Learning (AML) addresses vulnerabilities in AI systems where adversaries manipulate inputs or training data to degrade performance. This article provides a comprehensive analysis of evasion and poisoning attacks, formalizes defense mechanisms with mathematical rigor, and discusses the challenges of implementing robust solutions in adaptive threat models. Additionally, it highlights open challenges in certified robustness, scalability, and real-world deployment.