  For sensitive problems, such as medical imaging or fraud detection, Neural
Network (NN) adoption has been slow due to concerns about their reliability,
leading to a number of algorithms for explaining their decisions. NNs have also
been found vulnerable to a class of imperceptible attacks, called adversarial
examples, which arbitrarily alter the output of the network. Here we
demonstrate both that these attacks can invalidate prior attempts to explain
the decisions of NNs, and that with very robust networks, the attacks
themselves may be leveraged as explanations with greater fidelity to the model.
We show that the introduction of a novel regularization technique inspired by
the Lipschitz constraint, alongside other proposed improvements, greatly
improves an NN's resistance to adversarial examples. On the ImageNet
classification task, we demonstrate a network with an Accuracy-Robustness Area
(ARA) of 0.0053, an ARA 2.4x greater than the previous state of the art.
Improving the mechanisms by which NN decisions are understood is an important
direction for both establishing trust in sensitive domains and learning more
about the stimuli to which NNs respond.
