Turn Passive to Active: A Survey on Active Intellectual Property
  Protection of Deep Learning Models
Mingfu Xue Leo Yu Zhang Yushu Zhang Weiqiang Liu
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.