Adversarial training is a technique in machine learning aimed at enhancing the robustness of models against adversarial examples—inputs intentionally designed to deceive the model. This approach involves incorporating adversarial examples into the training process, allowing the model to learn from these challenging inputs. There are two primary methods: one involves retraining the model with previously identified adversarial examples, while the more effective method integrates perturbations directly into the training process. By doing so, adversarial training helps improve a model’s generalization capabilities, making it more resilient to various types of input manipulations, particularly in fields like natural language processing and computer vision.