  Adversarial examples, which are slightly perturbed inputs generated with the
aim of fooling a neural network, are known to transfer between models;
adversaries which are effective on one model will often fool another. This
concept of transferability poses grave security concerns as it leads to the
possibility of attacking models in a black box setting, during which the
internal parameters of the target model are unknown. In this paper, we seek to
analyze and minimize the transferability of adversaries between models within
an ensemble. To this end, we introduce a gradient based measure of how
effectively an ensemble's constituent models collaborate to reduce the space of
adversarial examples targeting the ensemble itself. Furthermore, we demonstrate
that this measure can be utilized during training as to increase an ensemble's
robustness to adversarial examples.
