A Comprehensive Study of Adversarial Attacks, Défense Mechanisms, and Emerging Challenges in Machine Learning

Abstract

Adversarial Machine Learning (AML) aims to enhance the security and robustness of machine learning models deployed in critical domains such as healthcare, finance, and autonomous technologies. Despite their impressive performance, these models are susceptible to adversarial interventions—carefully crafted inputs designed to manipulate model outputs. Key attack vectors include evasion attacks, data poisoning, and model extraction, each targeting different phases of the machine learning workflow. Numerous defense mechanisms, including adversarial training, preprocessing techniques, and robust optimization methods, have been proposed; however, no single approach provides complete protection, as attackers continue to develop increasingly sophisticated strategies. This review systematically examines prominent adversarial attack methods, analyzes existing defensive techniques, and outlines future research directions such as achieving provable robustness, creating adaptive defense frameworks, enhancing model interpretability, and addressing ethical considerations to ensure trustworthy and secure AI systems.

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