  This paper makes a substantial step towards cloning the functionality of
black-box models by introducing a Machine learning (ML) architecture named Deep
Neural Trees (DNTs). This new architecture can learn to separate different
tasks of the black-box model, and clone its task-specific behavior. We propose
to train the DNT using an active learning algorithm to obtain faster and more
sample-efficient training. In contrast to prior work, we study a complex
"victim" black-box model based solely on input-output interactions, while at
the same time the attacker and the victim model may have completely different
internal architectures. The attacker is a ML based algorithm whereas the victim
is a generally unknown module, such as a multi-purpose digital chip, complex
analog circuit, mechanical system, software logic or a hybrid of these. The
trained DNT module not only can function as the attacked module, but also
provides some level of explainability to the cloned model due to the tree-like
nature of the proposed architecture.
