In the context of machine learning and artificial intelligence, adversarial examples are slightly modified input data that are crafted specifically to deceive an algorithm into making incorrect predictions or classifications. This is particularly relevant in domains like image recognition, where a subtle change to an image—imperceptible to humans—can lead a model to misidentify the content of the image. Research in this area has shown that even leading-edge AI systems can be significantly vulnerable to adversarial attacks, prompting discussions about the security implications for AI deployments.