Adversarial Training

Adversarial training is a technique used in machine learning to improve a model's robustness and security. It involves exposing the model to intentionally misleading or challenging examples during its training process. By learning to recognize and correctly classify these tricky inputs, the model becomes better at handling unexpected or adversarial situations in real-world applications. Essentially, it’s like training for a sports team by making them practice against tough opponents, ensuring they are prepared for a variety of challenges. This approach helps create more reliable and resilient AI systems.