Adversarial Machine Learning - Attacks and Defense: Analyzing adversarial machine learning attacks and defense mechanisms to enhance the robustness of AI systems

Abstract

Adversarial machine learning (AML) has emerged as a critical area of research due to its potential to undermine the reliability and security of AI systems. This paper provides a comprehensive analysis of AML attacks and defense mechanisms, aiming to enhance the robustness of AI systems against adversarial attacks. We first introduce the concept of AML and discuss its implications for various applications, highlighting the need for robust defense strategies. We then categorize AML attacks into evasion, poisoning, and inference attacks, discussing their characteristics and potential impact on AI systems. Next, we review existing defense mechanisms, including adversarial training, defensive distillation, and gradient masking, analyzing their effectiveness and limitations. Additionally, we examine the role of transferability and robust optimization in enhancing the resilience of AI systems against adversarial attacks. Finally, we discuss future research directions and challenges in AML, emphasizing the importance of interdisciplinary approaches and collaboration to address the evolving threats posed by adversarial attacks.