  With the broad application of deep neural networks, the necessity of
protecting them as intellectual properties has become evident. Numerous
watermarking schemes have been proposed to identify the owner of a deep neural
network and verify the ownership. However, most of them focused on the
watermark embedding rather than the protocol for provable verification. To
bridge the gap between those proposals and real-world demands, we study the
deep learning model intellectual property protection in three scenarios: the
ownership proof, the federated learning, and the intellectual property
transfer. We present three protocols respectively. These protocols raise
several new requirements for the bottom-level watermarking schemes.
