  Neural Network classifiers have been used successfully in a wide range of
applications. However, their underlying assumption of attack free environment
has been defied by adversarial examples. Researchers tried to develop defenses;
however, existing approaches are still far from providing effective solutions
to this evolving problem. In this paper, we design a generative adversarial net
(GAN) based zero knowledge adversarial training defense, dubbed ZK-GanDef,
which does not consume adversarial examples during training. Therefore,
ZK-GanDef is not only efficient in training but also adaptive to new
adversarial examples. This advantage comes at the cost of small degradation in
test accuracy compared to full knowledge approaches. Our experiments show that
ZK-GanDef enhances test accuracy on adversarial examples by up-to 49.17%
compared to zero knowledge approaches. More importantly, its test accuracy is
close to that of the state-of-the-art full knowledge approaches (maximum
degradation of 8.46%), while taking much less training time.
