  Deep learning models are vulnerable to adversarial examples which are input
samples modified in order to maximize the error on the system. We introduce
Spartan Networks, resistant deep neural networks that do not require input
preprocessing nor adversarial training. These networks have an adversarial
layer designed to discard some information of the network, thus forcing the
system to focus on relevant input. This is done using a new activation function
to discard data. The added layer trains the neural network to filter-out
usually-irrelevant parts of its input. Our performance evaluation shows that
Spartan Networks have a slightly lower precision but report a higher robustness
under attack when compared to unprotected models. Results of this study of
Adversarial AI as a new attack vector are based on tests conducted on the MNIST
dataset.
