Example Concept: Adversarial training involves augmenting the training dataset with adversarial examples — inputs intentionally designed to deceive the model. By including these examples during the training phase, the model learns to recognize and correctly classify manipulated inputs, thereby increasing its resilience against adversarial attacks. This technique is part of a broader strategy to improve model robustness and is often complemented by input validation and anomaly detection methods.