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. This helps the model learn to recognize and correctly classify these altered 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 the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) are commonly employed to generate these adversarial examples. By training on these examples, models can achieve better accuracy and lower error rates when faced with adversarial inputs ²⁵.

However, adversarial training can be computationally intensive, as it often requires multiple forward-backward passes through the network for each weight update. Despite this, various methods have been proposed to mitigate the computational burden while still applying adversarial perturbations effectively ¹. Overall, adversarial training is a critical aspect of developing robust machine learning systems, especially in applications where security is paramount.