Adversarial Machine Learning: Security Threats and Mitigations

Abstract

Adversarial machine learning (AML) has emerged as a critical security concern in the deployment of AI-driven systems. Adversarial attacks exploit vulnerabilities in machine learning models by introducing subtle, often imperceptible, perturbations to input data, leading to misclassifications or erroneous predictions. These attacks can have severe consequences in sensitive domains such as autonomous driving, healthcare, finance, and cybersecurity, where the reliability of AI systems is paramount. This paper provides an in-depth analysis of adversarial attacks, classifying them into various types, including white-box, black-box, evasion, and poisoning attacks. It explores the real-world impact of these attacks and examines mitigation strategies such as adversarial training, defensive distillation, input preprocessing, and detection mechanisms. Furthermore, the paper highlights the ongoing challenge of adaptive attacks that evolve to bypass existing defenses.