Abstract: Artificial Intelligence has been well applied in many fields at present. However, the classification of neural network model output errors can be achieved by adversarial example. It is of great significance to study how to improve the robustness of the neural network model while taking into account the efficiency of the algorithm operation. To solve the above problems, this paper proposes a defense method Defense-CGAN based on conditional countermeasure generation network. Firstly, the generator of CGAN is used to generate the reconstructed image according to the input noise and label information, and then the MSE is used to extract the image features before and after the reconstruction. The reconstructed image is selected and fed to the classifier for classification, so as to remove the antagonistic perturbation and realize defense of the adversarial example. Finally, a large number of experiments are carried out on the MNIST data set. The experimental results show that the proposed defense method is more versatile, can defend against various kinds of adversarial attacks, and the time consumption is low at the same time. Therefore, this method can be applied to the real scene with extremely strict time requirement.

Key words: adversarial examples, neural network, adversarial example defense, conditional generative adversarial network, deep learning