  We explore adversarial robustness in the setting in which it is acceptable
for a classifier to abstain---that is, output no class---on adversarial
examples. Adversarial examples are small perturbations of normal inputs to a
classifier that cause the classifier to give incorrect output; they present
security and safety challenges for machine learning systems. In many
safety-critical applications, it is less costly for a classifier to abstain on
adversarial examples than to give incorrect output for them. We first introduce
a novel objective function for adversarial robustness with an abstain option
which characterizes an explicit tradeoff between robustness and accuracy. We
then present a simple baseline in which an adversarially-trained classifier
abstains on all inputs within a certain distance of the decision boundary,
which we theoretically and experimentally evaluate. Finally, we propose
Combined Abstention Robustness Learning (CARL), a method for jointly learning a
classifier and the region of the input space on which it should abstain. We
explore different variations of the PGD and DeepFool adversarial attacks on
CARL in the abstain setting. Evaluating against these attacks, we demonstrate
that training with CARL results in a more accurate, robust, and efficient
classifier than the baseline.

Machine Learning;Artificial Intelligence;Machine Learning