Adversarial training is a technique used in machine learning to improve the robustness of models against adversarial attacks. In this process, models are trained not only on regular data but also on intentionally modified inputs, known as adversarial examples, which are designed to confuse the model. This helps the model learn to recognize and correctly classify both normal and adversarial inputs. By incorporating adversarial examples into the training process, models become better at generalizing and maintaining performance in real-world scenarios where they might encounter unexpected or malicious inputs. This approach is particularly important in fields like computer vision and natural language processing, where security and reliability are crucial.