  Protecting the Intellectual Property Rights (IPR) associated to Deep Neural
Networks (DNNs) is a pressing need pushed by the high costs required to train
such networks and the importance that DNNs are gaining in our society.
Following its use for Multimedia (MM) IPR protection, digital watermarking has
recently been considered as a mean to protect the IPR of DNNs. While DNN
watermarking inherits some basic concepts and methods from MM watermarking,
there are significant differences between the two application areas, calling
for the adaptation of media watermarking techniques to the DNN scenario and the
development of completely new methods. In this paper, we overview the most
recent advances in DNN watermarking, by paying attention to cast it into the
bulk of watermarking theory developed during the last two decades, while at the
same time highlighting the new challenges and opportunities characterizing DNN
watermarking. Rather than trying to present a comprehensive description of all
the methods proposed so far, we introduce a new taxonomy of DNN watermarking
and present a few exemplary methods belonging to each class. We hope that this
paper will inspire new research in this exciting area and will help researchers
to focus on the most innovative and challenging problems in the field.
