Which of the following best defines adversarial training in Generative AI?

Adversarial training is best defined as a method where models learn to withstand deceptive inputs. In the context of Generative AI, it involves training machine learning models to recognize and counteract adversarial examples—inputs specifically designed to fool the models into making incorrect predictions or classifications. By exposing the model to these types of inputs during the training process, it becomes more robust and capable of handling perturbations that could mislead it. This technique is essential for improving the reliability and effectiveness of AI systems, particularly in applications where security and precision are critical.

In contrast, the other options focus on entirely different aspects unrelated to adversarial training. Simplifying model architecture pertains to designing models that are more efficient but does not directly address the issue of deceptive inputs. Enhancing data encryption is centered around the security of data rather than the integrity of model predictions. Increasing data storage capabilities is a measure related to managing data resources, which also does not involve the concept of adversarial inputs or training models to defend against them.