Single Step Adversarial Training

Single-step adversarial training aims to improve the robustness of deep learning models against adversarial attacks by efficiently incorporating adversarial examples into the training process. Current research focuses on mitigating "catastrophic overfitting," a phenomenon where models trained with single-step methods become unexpectedly vulnerable to stronger, multi-step attacks, often through techniques like adding noise to training samples or employing novel regularization methods to enforce local linearity in the loss function. These advancements enhance the efficiency and reliability of adversarial training, impacting the development of more robust and secure machine learning systems across various applications.