Adversarial Machine Learning: Securing AI Models against Evasion Attacks through Adaptive Defense Mechanisms

Abstract

As artificial intelligence systems become integral to critical applications across various sectors, their susceptibility to adversarial evasion attacks raises significant security concerns. This paper delves into the complexities of adversarial machine learning, focusing on strategies for securing AI models against such attacks. By integrating concepts of model robustness, interpretability, and adaptive defense mechanisms, the study aims to propose a comprehensive framework for enhancing AI resilience. Through a systematic review of existing methodologies, evaluation of innovative defensive strategies, and empirical validation, this research highlights the multifaceted nature of securing AI systems and aims to pave the way for more secure and reliable AI applications.