Biblio

Deep Neural Networks (DNNs) have been widely used in variety of fields with great success. However, recent researches indicate that DNNs are susceptible to adversarial attacks, which can easily fool the well-trained DNNs without being detected by human eyes. In this paper, we propose to combine the target DNN model with robust bit plane classifiers to defend against adversarial attacks. It comes from our finding that successful attacks generate imperceptible perturbations, which mainly affects the low-order bits of pixel value in clean images. Hence, using bit planes instead of traditional RGB channels for convolution can effectively reduce channel modification rate. We conduct experiments on dataset CIFAR-10 and GTSRB. The results show that our defense method can effectively increase the model accuracy on average from 8.72% to 85.99% under attacks on CIFAR-10 without sacrificina accuracy of clean images.