Adversarial Examples Generation and Defense Based on Generative Adversarial Network
We propose a novel generative adversarial network to generate and defend adversarial examples for deep neural networks (DNN) The adversarial stability of a network D is improved by training alternatively with an additional network G. Our experiment is carried out on MNIST, and the adversarial examples are generated in an efficient way compared with wildly used gradient based methods. After training network D and G alternatively, complicated adversarial patterns are extracted. The target network D is demonstrated to become robust against the adversarial attack from G. Our approach provides a broadly new viewpoint for the adversarial instability researches, and have various critical applications in practice.