  Recent research showed that deep neural networks are highly sensitive to
so-called adversarial perturbations, which are tiny perturbations of the input
data purposely designed to fool a machine learning classifier. Most
classification models, including deep learning models, are highly vulnerable to
adversarial attacks. In this work, we investigate a procedure to improve
adversarial robustness of deep neural networks through enforcing representation
invariance. The idea is to train the classifier jointly with a discriminator
attached to one of its hidden layer and trained to filter the adversarial
noise. We perform preliminary experiments to test the viability of the approach
and to compare it to other standard adversarial training methods.
