Final answer:
An adversarial model uses perturbed inputs to test the robustness of machine learning algorithms by observing different outputs when these slight alterations are made to the input data.
Step-by-step explanation:
An adversarial model relies on using B: Perturbed inputs to observe different outputs. This concept mainly applies to the field of machine learning, specifically in the context of adversarial machine learning. An adversarial model aims to evaluate or attack a machine learning system by making intentional, small, and often imperceptible changes to the input data (perturbations) to confuse the model into making incorrect predictions or classifications. This technique is particularly useful for testing the robustness of an algorithm against potential malicious attacks or unintended biases that can result in model failure or performance degradation.