The intellectual property protection of deep learning (DL) models has attracted increasing serious concerns. Many works on intellectual property protection for Deep Neural Networks (DNN) models have been proposed. The vast majority of existing work uses DNN watermarking to verify the ownership of the model after piracy occurs, which is referred to as passive verification. On the contrary, we focus on a new type of intellectual property protection method named active copyright protection, which refers to active authorization control and user identity management of the DNN model. As of now, there is relatively limited research in the field of active DNN copyright protection. In this review, we attempt to clearly elaborate on the connotation, attributes, and requirements of active DNN copyright protection, provide evaluation methods and metrics for active copyright protection, review and analyze existing work on active DL model intellectual property protection, discuss potential attacks that active DL model copyright protection techniques may face, and provide challenges and future directions for active DL model intellectual property protection. This review is helpful to systematically introduce the new field of active DNN copyright protection and provide reference and foundation for subsequent work.

翻译：深度学习模型的知识产权保护问题日益受到关注。目前已有大量关于深度神经网络模型知识产权保护的研究工作。现有工作绝大多数采用深度神经网络水印技术，在模型被盗用后进行所有权验证，这被称为被动验证。相反，我们聚焦于一种新型知识产权保护方法——主动版权保护，即对深度神经网络模型进行主动授权控制和用户身份管理。目前，主动深度神经网络版权保护领域的研究相对有限。本综述旨在清晰阐述主动深度神经网络版权保护的内涵、属性与需求，提供主动版权保护的评估方法与度量指标，回顾并分析现有主动深度学习模型知识产权保护工作，探讨主动深度学习模型版权保护技术可能面临的潜在攻击，并提出主动深度学习模型知识产权保护的挑战与未来方向。本综述有助于系统性地介绍主动深度神经网络版权保护这一新领域，并为后续工作提供参考与基础。