Stylized Pairing for Robust Adversarial Defense

Abstract

Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two phenomenons, we conjecture that the preference of DNNs on exploiting object textures for decisions is one of the most important reasons for the existence of adversarial examples. At present, most adversarial defense methods are directly related to adversarial perturbations. In this paper, we propose an adversarial defense method independent of adversarial perturbations, which utilizes a stylized pairing technique to encourage logits for a pair of images and the corresponding stylized image to be similar. With stylized pairing training, DNNs can better learn shape-biased representation. We have empirically evaluated the performance of our method through extensive experiments on CIFAR10, CIFAR100, and ImageNet datasets. Results show that the models with stylized pairing training can significantly improve their performance against adversarial examples.