metarobust
Metarobust is a term that emerged in the field of artificial intelligence, specifically related to machine learning and adversarial attacks. It describes a system or algorithm that is designed to be robust against adversarial perturbations, which are small, often imperceptible changes made to input data that can cause a machine learning model to misclassify or behave unexpectedly. The "meta" prefix suggests a higher level of robustness, implying a system that not only resists current adversarial attacks but also has mechanisms to adapt or remain robust against future, potentially unknown, attack strategies.
Developing metarobust systems often involves techniques beyond standard training methods. This can include adversarial training, where