Towards Speeding up Adversarial Training in Latent Spaces

Understand

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.