An adversarial example is a type of input designed to fool a machine learning model, particularly in the field of artificial intelligence. These inputs are often slightly altered versions of normal data, such as images or text, that cause the model to make incorrect predictions or classifications. For instance, a small change in the pixels of an image can lead a model to misidentify an object, even though the alteration is imperceptible to humans. Researchers study adversarial examples to understand the vulnerabilities of models like neural networks and improve their robustness. By generating these examples, they can test how well a model can withstand unexpected inputs, which is crucial for applications in areas like computer vision and natural language processing.