  In image classification of deep learning, adversarial examples where inputs
intended to add small magnitude perturbations may mislead deep neural networks
(DNNs) to incorrect results, which means DNNs are vulnerable to them. Different
attack and defense strategies have been proposed to better research the
mechanism of deep learning. However, those research in these networks are only
for one aspect, either an attack or a defense, not considering that attacks and
defenses should be interdependent and mutually reinforcing, just like the
relationship between spears and shields. In this paper, we propose
Cycle-Consistent Adversarial GAN (CycleAdvGAN) to generate adversarial
examples, which can learn and approximate the distribution of original
instances and adversarial examples. For CycleAdvGAN, once the Generator and are
trained, can generate adversarial perturbations efficiently for any instance,
so as to make DNNs predict wrong, and recovery adversarial examples to clean
instances, so as to make DNNs predict correct. We apply CycleAdvGAN under
semi-white box and black-box settings on two public datasets MNIST and CIFAR10.
Using the extensive experiments, we show that our method has achieved the
state-of-the-art adversarial attack method and also efficiently improve the
defense ability, which make the integration of adversarial attack and defense
come true. In additional, it has improved attack effect only trained on the
adversarial dataset generated by any kind of adversarial attack.
