  Convolutional neural networks (CNNs) have achieved beyond human-level
accuracy in the image classification task and are widely deployed in real-world
environments. However, CNNs show vulnerability to adversarial perturbations
that are well-designed noises aiming to mislead the classification models. In
order to defend against the adversarial perturbations, adversarially trained
GAN (ATGAN) is proposed to improve the adversarial robustness generalization of
the state-of-the-art CNNs trained by adversarial training. ATGAN incorporates
adversarial training into standard GAN training procedure to remove obfuscated
gradients which can lead to a false sense in defending against the adversarial
perturbations and are commonly observed in existing GANs-based adversarial
defense methods. Moreover, ATGAN adopts the image-to-image generator as data
augmentation to increase the sample complexity needed for adversarial
robustness generalization in adversarial training. Experimental results in
MNIST SVHN and CIFAR-10 datasets show that the proposed method doesn't rely on
obfuscated gradients and achieves better global adversarial robustness
generalization performance than the adversarially trained state-of-the-art
CNNs.
