Carlini & Wagner Attack

The Carlini & Wagner Attack is a method used to create adversarial examples, which are inputs that can fool machine learning models like image classifiers. By making subtle, almost imperceptible changes to an image, this attack can trick a model into misclassifying it. For instance, a picture of a cat could be altered slightly to be misidentified as a dog. This method demonstrates vulnerabilities in AI systems, highlighting the need for improved defenses against such deceptive inputs to ensure reliable and secure AI applications.