How are adversarial examples generated and used to evaluate the robustness of machine learning models?

Adversarial examples are crafted by making small, imperceptible perturbations to input data with the goal of causing misclassification or erroneous predictions by machine learning models. Adversarial examples are used to evaluate the robustness of models against adversarial attacks and to develop defense mechanisms such as adversarial training and robust optimization techniques.