Adversarial training is a technique used in machine learning to enhance the robustness of models against adversarial attacks. The core idea is to incorporate adversarial examples—inputs that have been intentionally perturbed to mislead the model—into the training process. By doing so, the model learns to recognize and correctly classify these perturbed inputs, thereby improving its overall performance and resilience against such attacks ².

The process typically involves optimizing a loss function that accounts for both the original and adversarial examples. Techniques like Projected Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) are commonly employed to generate these adversarial examples. The goal is to minimize the maximum loss across all possible adversarial perturbations, ensuring that the model performs well regardless of the type of attack it encounters ²⁵.

Despite its benefits, adversarial training can be computationally intensive, as it often requires multiple forward-backward passes through the network for each weight update. However, various methods have been proposed to mitigate this computational burden while still achieving robust performance ¹².