Comprehensive Analysis of Adversarial Training Methods: Enhancing Model Resilience in High-Dimensional Spaces

Abstract

Machine learning models, particularly deep neural networks, have demonstrated remarkable success in various domains. However, their vulnerability to adversarial perturbations, imperceptible input modifications that can lead to misclassification, has emerged as a critical challenge. Adversarial training, a prominent defense strategy, has gained significant attention for enhancing model robustness against such attacks. This paper presents a comprehensive analysis of adversarial training methods, exploring their theoretical foundations, practical implementations, and implications in high-dimensional spaces. We delve into the trade-offs between robustness, accuracy, and computational complexity, highlighting the importance of carefully designed adversarial training regimes. Furthermore, we discuss the limitations and open challenges associated with these methods, emphasizing the need for continued research to develop more robust and secure machine learning systems.