  Adversarial training has been shown to regularize deep neural networks in
addition to increasing their robustness to adversarial examples. However, its
impact on very deep state of the art networks has not been fully investigated.
In this paper, we present an efficient approach to perform adversarial training
by perturbing intermediate layer activations and study the use of such
perturbations as a regularizer during training. We use these perturbations to
train very deep models such as ResNets and show improvement in performance both
on adversarial and original test data. Our experiments highlight the benefits
of perturbing intermediate layer activations compared to perturbing only the
inputs. The results on CIFAR-10 and CIFAR-100 datasets show the merits of the
proposed adversarial training approach. Additional results on WideResNets show
that our approach provides significant improvement in classification accuracy
for a given base model, outperforming dropout and other base models of larger
size.
