  In this work, we consider model robustness of deep neural networks against
adversarial attacks from a global manifold perspective. Leveraging both the
local and global latent information, we propose a novel adversarial training
method through robust optimization, and a tractable way to generate Latent
Manifold Adversarial Examples (LMAEs) via an adversarial game between a
discriminator and a classifier. The proposed adversarial training with latent
distribution (ATLD) method defends against adversarial attacks by crafting
LMAEs with the latent manifold in an unsupervised manner. ATLD preserves the
local and global information of latent manifold and promises improved
robustness against adversarial attacks. To verify the effectiveness of our
proposed method, we conduct extensive experiments over different datasets
(e.g., CIFAR-10, CIFAR-100, SVHN) with different adversarial attacks (e.g.,
PGD, CW), and show that our method substantially outperforms the
state-of-the-art (e.g., Feature Scattering) in adversarial robustness by a
large accuracy margin. The source codes are available at
https://github.com/LitterQ/ATLD-pytorch.
