Adversarial Attacks

Adversarial attacks refer to techniques used to trick machine learning models, like image recognition systems, into making mistakes. This is often done by slightly altering the input data—such as adding noise to an image or tweaking features—so that the model misclassifies it. For example, a picture of a panda might be modified just enough for the model to see it as a gibbon. These attacks highlight vulnerabilities in artificial intelligence systems, raising concerns about their reliability and safety, especially in critical applications like security or autonomous driving.