Generalizing universal adversarial perturbations for deep neural networks

This paper proposes a novel unified yet flexible framework for universal adversarial attacks, called GUAP, which can initiate attacks by

$$\ell _\infty$$

ℓ ∞ -norm (additive) perturbation, spatially-transformed (non-additive), or a combination of both.