  Although deep generative models such as Defense-GAN and Defense-VAE have made
significant progress in terms of adversarial defenses of image classification
neural networks, several methods have been found to circumvent these defenses.
Based on Defense-VAE, in our research we introduce several methods to improve
the robustness of defense models. The methods introduced in this paper are
straight forward yet show promise over the vanilla Defense-VAE. With extensive
experiments on MNIST data set, we have demonstrated the effectiveness of our
algorithms against different attacks. Our experiments also include attacks on
the latent space of the defensive model. We also discuss the applicability of
existing adversarial latent space attacks as they may have a significant flaw.
