Adversarial Machine Learning: Attacks, Defences, and the Road Ahead

Abstract Adversarial Machine Learning (AML) represents a growing field of study focusing on the vulnerabilities of machine learning models to maliciously crafted inputs. This paper provides a comprehensive overview of AML, including attack types, defence mechanisms, and a detailed evaluation framework. By combining simulated experiments with real-world datasets, expert interviews, and comparative benchmarking, we investigate the performance, robustness, and feasibility of various adversarial strategies and countermeasures. Our findings reveal critical trade-offs between model accuracy and robustness, suggesting a need for adaptive, context-aware defences in AI systems.