  Although neural networks could achieve state-of-the-art performance while
recongnizing images, they often suffer a tremendous defeat from adversarial
examples--inputs generated by utilizing imperceptible but intentional
perturbation to clean samples from the datasets. How to defense against
adversarial examples is an important problem which is well worth researching.
So far, very few methods have provided a significant defense to adversarial
examples. In this paper, a novel idea is proposed and an effective framework
based Generative Adversarial Nets named APE-GAN is implemented to defense
against the adversarial examples. The experimental results on three benchmark
datasets including MNIST, CIFAR10 and ImageNet indicate that APE-GAN is
effective to resist adversarial examples generated from five attacks.
