  Tree models are very widely used in practice of machine learning and data
mining. In this paper, we study the problem of model integrity authentication
in tree models. In general, the task of model integrity authentication is the
design \& implementation of mechanisms for checking/detecting whether the model
deployed for the end-users has been tampered with or compromised, e.g.,
malicious modifications on the model. We propose an authentication framework
that enables the model builders/distributors to embed a signature to the tree
model and authenticate the existence of the signature by only making a small
number of black-box queries to the model. To the best of our knowledge, this is
the first study of signature embedding on tree models. Our proposed method
simply locates a collection of leaves and modifies their prediction values,
which does not require any training/testing data nor any re-training. The
experiments on a large number of public classification datasets confirm that
the proposed signature embedding process has a high success rate while only
introducing a minimal prediction accuracy loss.
