What is adversarial machine learning robustness?

Adversarial machine learning robustness refers to the ability of a machine learning model to maintain its performance and resist manipulation when exposed to adversarial examples—inputs that have been intentionally modified to deceive the model. In this field, researchers and engineers develop techniques to detect, defend against, and reduce the impact of such attacks on AI systems. Robust models are crucial in applications like security, autonomous vehicles, and healthcare, where adversarial attacks could have serious consequences. Achieving robustness involves strategies like adversarial training, input preprocessing, and model architecture improvements.