We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularization methods such as Dropout. We applied our method to supervised learning for CIFAR datasets, and experimantal results show that our method significantly lowers the generalization error of the network. To the best of our knowledge, this is the first method which uses GAN to improve supervised learning.

翻译：我们提出一种新颖的技术，利用生成对抗网络使神经网络对对抗性样本具有鲁棒性。我们交替训练分类器和生成器网络。生成器网络通过利用每个图像的梯度，生成易于欺骗分类器网络的对抗性扰动。同时，分类器网络被训练为能正确分类由生成器产生的原始图像和对抗性图像。这些过程有助于分类器网络对对抗性扰动变得更加鲁棒。此外，我们的对抗训练框架有效减少了过拟合，并优于Dropout等其他正则化方法。我们将该方法应用于CIFAR数据集的监督学习，实验结果表明，我们的方法显著降低了网络的泛化误差。据我们所知，这是首个利用GAN改进监督学习的方法。