  Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made
revolutionary progress in recent years, and are widely used in various fields.
The high performance of DNNs requires a huge amount of high-quality data,
expensive computing hardware, and excellent DNN architectures that are costly
to obtain. Therefore, trained DNNs are becoming valuable assets and must be
considered the Intellectual Property (IP) of the legitimate owner who created
them, in order to protect trained DNN models from illegal reproduction,
stealing, redistribution, or abuse. Although being a new emerging and
interdisciplinary field, numerous DNN model IP protection methods have been
proposed. Given this period of rapid evolution, the goal of this paper is to
provide a comprehensive survey of two mainstream DNN IP protection methods:
deep watermarking and deep fingerprinting, with a proposed taxonomy. More than
190 research contributions are included in this survey, covering many aspects
of Deep IP Protection: problem definition, main threats and challenges, merits
and demerits of deep watermarking and deep fingerprinting methods, evaluation
metrics, and performance discussion. We finish the survey by identifying
promising directions for future research.
