Regularized Intermediate Layers Attack: Adversarial Examples With High Transferability

Abstract

Although convolutional neural network (CNN) models have demonstrated state-of-the-art performance, especially in many image classification and recognition tasks, the classification accuracy would significantly decreased in the adversarial image samples set by adding slight perturbations in the input images. Currently, many adversarial examples were designed for specific CNNs but they were not universal valid across different CNNs. In this paper, we proposed a new intermediate layer optimization method to ensure that the adversarial examples are effective across different CNN models. Given one image, the proposed algorithm can derive multiple adversarial examples from just one white-box adversarial example by analyzing its regularized features in the intermediate layers of the attacked CNN. The adversarial examples derived from the intermediate layers showed better transferability compared with the original white-box adversarial example. According to the experiments on multiple CNN models, our algorithm promotes the averaging transfer attacking success rate (ASR) by 10.5% and 4.81%, compared to the baseline white-box attacking methods and the recent intermediate layer based attacking method ILA respectively.