An adversarial attack is a technique used to deceive machine learning models, particularly in areas like image recognition. By making small, often imperceptible changes to input data, attackers can cause models to misclassify or misinterpret the information. For example, a picture of a cat might be altered slightly so that a model identifies it as a dog instead. These attacks highlight vulnerabilities in artificial intelligence systems, raising concerns about their reliability and security. Researchers are actively working on methods to defend against adversarial attacks, ensuring that models remain robust and accurate in real-world applications, such as self-driving cars and facial recognition systems.