  Measuring uncertainty is a promising technique for detecting adversarial
examples, crafted inputs on which the model predicts an incorrect class with
high confidence. But many measures of uncertainty exist, including predictive
en- tropy and mutual information, each capturing different types of
uncertainty. We study these measures, and shed light on why mutual information
seems to be effective at the task of adversarial example detection. We
highlight failure modes for MC dropout, a widely used approach for estimating
uncertainty in deep models. This leads to an improved understanding of the
drawbacks of current methods, and a proposal to improve the quality of
uncertainty estimates using probabilistic model ensembles. We give illustrative
experiments using MNIST to demonstrate the intuition underlying the different
measures of uncertainty, as well as experiments on a real world Kaggle dogs vs
cats classification dataset.
