Learning to Generate Cross-Task Unexploitable Examples

This article likely discusses a novel approach to creating adversarial examples for machine learning models. The focus is on generating examples that are robust across different tasks, making them more effective in testing and potentially improving model security. The use of 'unexploitable' suggests an attempt to create examples that cannot be easily circumvented or used to compromise the model.