With the widespread application in industrial manufacturing and commercial
services, well-trained deep neural networks (DNNs) are becoming increasingly
valuable and crucial assets due to the tremendous training cost and excellent
generalization performance. These trained models can be utilized by users
without much expert knowledge benefiting from the emerging ''Machine Learning
as a Service'' (MLaaS) paradigm. However, this paradigm also exposes the
expensive models to various potential threats like model stealing and abuse. As
an urgent requirement to defend against these threats, Deep Intellectual
Property (DeepIP), to protect private training data, painstakingly-tuned
hyperparameters, or costly learned model weights, has been the consensus of
both industry and academia. To this end, numerous approaches have been proposed
to achieve this goal in recent years, especially to prevent or discover model
stealing and unauthorized redistribution. Given this period of rapid evolution,
the goal of this paper is to provide a comprehensive survey of the recent
achievements in this field. More than 190 research contributions are included
in this survey, covering many aspects of Deep IP Protection:
challenges/threats, invasive solutions (watermarking), non-invasive solutions
(fingerprinting), evaluation metrics, and performance. We finish the survey by
identifying promising directions for future research.

本文综述了深度知识产权保护的最新进展，包括挑战 / 威胁，水印技术，指纹技术，评估指标和性能等方面，并为未来的研究提出了有希望的方向。