Abstract: Adversarial training is wildly considered as the most effective way to defend
against adversarial examples. However, existing adversarial training methods
consume unbearable time cost, since they need to generate adversarial examples
in the input space, which accounts for the main part of total time-consuming.
For speeding up the training process, we propose a novel adversarial training
method that does not need to generate real adversarial examples. We notice that
a clean example is closer to the decision boundary of the class with the second
largest logit component than any other class besides its own class. Thus, by
adding perturbations to logits to generate Endogenous Adversarial
Examples(EAEs) -- adversarial examples in the latent space, it can avoid
calculating gradients to speed up the training process. We further gain a deep
insight into the existence of EAEs by the theory of manifold. To guarantee the
added perturbation is within the range of constraint, we use statistical
distributions to select seed examples to craft EAEs. Extensive experiments are
conducted on CIFAR-10 and ImageNet, and the results show that compare with
state-of-the-art "Free" and "Fast" methods, our EAE adversarial training not
only shortens the training time, but also enhances the robustness of the model.
Moreover, the EAE adversarial training has little impact on the accuracy of
clean examples than the existing methods.