We introduce a novel artificial neural network architecture that integrates robustness to adversarial input in the network structure. The main idea of our approach is to force the network to make predictions on what the given instance of the class under consideration would look like and subsequently test those predictions. By forcing the network to redraw the relevant parts of the image and subsequently comparing this new image to the original, we are having the network give a "proof" of the presence of the object.
@article{arxiv.1604.04428,
title = {The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection},
author = {Harm Berntsen and Wouter Kuijper and Tom Heskes},
journal= {arXiv preprint arXiv:1604.04428},
year = {2016}
}