ROBUSTNESS OF DEEP NEURAL NETWORKS TO ADVERSARIAL EXAMPLES - Teng, Da; Song, Xiao; Gong, Guanghong; Han, liang (International Journal of Industrial Engineering: Theory, Applications and Practice, 2017)
Deep neural networks have achieved state-of-the-art performance in many artificial intelligence areas, such as object recognition, speech recognition and machine translation. While the deep neural networks have high expression capabilities, they are prone to over fitting due to the high dimensionalities of the networks. Recently, deep neural networks are found to be unstable to adversarial perturbations, which are small but can maximize the network’s prediction error. This paper proposes a novel training algorithm to improve the robustness of the neural networks to adversarial examples.