Adversarial training is a technique for improving a machine learning model's robustness by deliberately exposing it to adversarial examples during training, inputs specifically crafted to be difficult, misleading, or designed to trigger mistakes, rather than only training on typical, "clean" examples. By learning to handle these deliberately challenging cases, the model becomes more reliable when it later encounters genuinely tricky or unusual inputs in the real world.
This matters directly for security and reliability: without adversarial training, a model can sometimes be fooled by inputs that look almost identical to normal, correctly-handled examples but are subtly perturbed in ways a human wouldn't even notice, causing the model to make a confidently wrong prediction. Adversarial training is now a standard part of building models intended for high-stakes or security-sensitive applications, where an attacker might deliberately try to exploit exactly this kind of weakness.
