Defense Against Universal Adversarial Perturbations

Abstract

Recent advances in Deep Learning show the existence of image-agnostic quasi-imperceptible perturbations that when applied to 'any' image can fool a state-of-the-art network classifier to change its prediction about the image label. These 'Universal Adversarial Perturbations' pose a serious threat to the success of Deep Learning in practice. We present the first dedicated framework to effectively defend the networks against such perturbations. Our approach learns a Perturbation Rectifying Network (PRN) as 'pre-input' layers to a targeted model, such that the targeted model needs no modification. The PRN is learned from real and synthetic image-agnostic perturbations, where an efficient method..