Adaptive Adversarial Training Strategies for Increasing the Resilience of Machine Learning Models

Abstract

Adversarial training is a prominent approach in the realm of machine learning aimed at enhancing the robustness of models against adversarial attacks. This paper reviews various adversarial training strategies, their mechanisms, and effectiveness in mitigating different types of attacks. We discuss the evolution of adversarial training, key methodologies, and the challenges faced in deploying these strategies in practical scenarios. Our comparative analysis highlights the strengths and limitations of existing approaches and suggests directions for future research.