Adversarial Machine Learning & Defensive Countermeasures

Abstract

ABSTRACT Machine learning models are increasingly deployed in critical applications such as healthcare diagnostics, autonomous vehicles, biometric authentication, fraud detection, and financial systems. While these systems show remarkable performance, they are vulnerable to adversarial attacks that manipulate input data or training processes to degrade model performance or cause incorrect decisions. Adversarial Machine Learning (AML) studies these vulnerabilities and proposes defensive mechanisms to make systems more robust and reliable. This paper provides a detailed review of adversarial threats including evasion attacks, poisoning attacks, model extraction, and backdoor attacks. It also discusses defensive countermeasures such as adversarial training, defensive distillation, input preprocessing, robust optimization, and detection-based approaches. Furthermore, emerging research trends including certified robustness and game-theoretic defense models are examined. The study aims to provide a structured overview for researchers and practitioners working toward secure and trustworthy AI systems.

KEYWORDS: Adversarial attacks, Machine learning security, Evasion attack, Poisoning attack, Model robustness, Defensive countermeasures, Backdoor attacks, Certified defense