One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features

This work develops feature-level adversarial perturbations using deep image generators and a novel optimization objective that reveal spurious, semantically-describable feature/class associations that can be exploited by novel combinations of objects.