Adversarial Training and Beyond Approaches for Improving Deep Learning Model Robustness

Abstract

The rapid advancement of deep learning techniques has led to significant improvements in various domains, including computer vision, natural language processing, and autonomous systems. However, these models are vulnerable to adversarial attacks, where small, intentionally crafted perturbations can drastically alter their predictions. This paper explores the landscape of adversarial attacks and defenses in deep learning, presenting a comprehensive review of existing techniques, recent advancements, and future directions. By analyzing the effectiveness and limitations of current methods, we aim to contribute to the development of more robust deep learning systems.