  Tree ensembles are powerful models that are widely used. However, they are
susceptible to adversarial examples, which are examples that purposely
constructed to elicit a misprediction from the model. This can degrade
performance and erode a user's trust in the model. Typically, approaches try to
alleviate this problem by verifying how robust a learned ensemble is or
robustifying the learning process. We take an alternative approach and attempt
to detect adversarial examples in a post-deployment setting. We present a novel
method for this task that works by analyzing an unseen example's output
configuration, which is the set of predictions made by an ensemble's
constituent trees. Our approach works with any additive tree ensemble and does
not require training a separate model. We evaluate our approach on three
different tree ensemble learners. We empirically show that our method is
currently the best adversarial detection method for tree ensembles.
