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.

翻译：与深神经网络相关的知识产权保护(IPR)是一个紧迫的需要,因为培训这类网络需要高昂的费用,而且DNN正在我们的社会中获得重要性。在多媒体(MM)知识产权保护使用之后,数字水标记最近被视为保护DNNP知识产权的一种手段。虽然DNN水标记继承了MM水标记的一些基本概念和方法,但两个应用领域之间有很大的差别,要求媒体水标记技术适应DNN情景和开发全新的方法。在本文中,我们回顾DNN水标记方面的最新进展,注意将其纳入过去二十年开发的水标记理论的主体,同时强调DNNW水标记的新的挑战和机遇。我们不是试图全面描述迄今提出的所有方法,而是引入DNNN水标记的新分类,提出属于每一类的少数样板方法。我们希望,该文件将激发这个领域最具挑战性的领域的新研究,并有助于研究人员关注最令人兴奋的问题。