  Adversarial examples reveal the vulnerability and unexplained nature of
neural networks. Studying the defense of adversarial examples is of
considerable practical importance. Most adversarial examples that misclassify
networks are often undetectable by humans. In this paper, we propose a defense
model to train the classifier into a human-perception classification model with
shape preference. The proposed model comprising a texture transfer network
(TTN) and an auxiliary defense generative adversarial networks (GAN) is called
Human-perception Auxiliary Defense GAN (HAD-GAN). The TTN is used to extend the
texture samples of a clean image and helps classifiers focus on its shape. GAN
is utilized to form a training framework for the model and generate the
necessary images. A series of experiments conducted on MNIST, Fashion-MNIST and
CIFAR10 show that the proposed model outperforms the state-of-the-art defense
methods for network robustness. The model also demonstrates a significant
improvement on defense capability of adversarial examples.
