  Deep neural network (DNN) models are known to be vulnerable to maliciously
crafted adversarial examples and to out-of-distribution inputs drawn
sufficiently far away from the training data. How to protect a machine learning
model against deception of both types of destructive inputs remains an open
challenge. This paper presents XEnsemble, a diversity ensemble verification
methodology for enhancing the adversarial robustness of DNN models against
deception caused by either adversarial examples or out-of-distribution inputs.
XEnsemble by design has three unique capabilities. First, XEnsemble builds
diverse input denoising verifiers by leveraging different data cleaning
techniques. Second, XEnsemble develops a disagreement-diversity ensemble
learning methodology for guarding the output of the prediction model against
deception. Third, XEnsemble provides a suite of algorithms to combine input
verification and output verification to protect the DNN prediction models from
both adversarial examples and out of distribution inputs. Evaluated using
eleven popular adversarial attacks and two representative out-of-distribution
datasets, we show that XEnsemble achieves a high defense success rate against
adversarial examples and a high detection success rate against
out-of-distribution data inputs, and outperforms existing representative
defense methods with respect to robustness and defensibility.
