Production artificial intelligence (AI) systems face adversarial examples that exploit precise mathematical vulnerabilities in model decision boundaries, requiring perturbations crafted with exact pixel or signal values to maintain effectiveness. Traditional defensive approaches focus on improving model robustness through retraining, but preprocessing defenses intercept threats before they reach vulnerable models by transforming inputs in ways that disrupt adversarial perturbations while preserving legitimate content.