Traditional machine learning (ML) training optimizes for clean accuracy on benign inputs, leaving models fundamentally vulnerable to adversarial examples specifically crafted to cause misclassification. Robust training techniques address this vulnerability at the source by incorporating adversarial perturbations directly into the training process, producing models that maintain accurate predictions even when inputs are deliberately manipulated by sophisticated attackers.