  Adversarial training is wildly considered as one of the most effective way to
defend against adversarial examples. However, existing adversarial training
methods consume unbearable time, due to the fact that they need to generate
adversarial examples in the large input space. To speed up adversarial
training, we propose a novel adversarial training method that does not need to
generate real adversarial examples. By adding perturbations to logits to
generate Endogenous Adversarial Examples (EAEs) -- the adversarial examples in
the latent space, the time consuming gradient calculation can be avoided.
Extensive experiments are conducted on CIFAR-10 and ImageNet, and the results
show that comparing to state-of-the-art methods, our EAE adversarial training
not only shortens the training time, but also enhances the robustness of the
model and has less impact on the accuracy of clean examples than the existing
methods.

Machine Learning;Artificial Intelligence