  Deep neural networks (DNNs) have achieved tremendous success in artificial
intelligence (AI) fields. However, DNN models can be easily illegally copied,
redistributed, or abused by criminals, seriously damaging the interests of
model inventors. The copyright protection of DNN models by neural network
watermarking has been studied, but the establishment of a traceability
mechanism for determining the authorized users of a leaked model is a new
problem driven by the demand for AI services. Because the existing traceability
mechanisms are used for models without watermarks, a small number of
false-positives are generated. Existing black-box active protection schemes
have loose authorization control and are vulnerable to forgery attacks.
Therefore, based on the idea of black-box neural network watermarking with the
video framing and image perceptual hash algorithm, a passive copyright
protection and traceability framework PCPT is proposed that uses an additional
class of DNN models, improving the existing traceability mechanism that yields
a small number of false-positives. Based on an authorization control strategy
and image perceptual hash algorithm, a DNN model active copyright protection
and traceability framework ACPT is proposed. This framework uses the
authorization control center constructed by the detector and verifier. This
approach realizes stricter authorization control, which establishes a strong
connection between users and model owners, improves the framework security, and
supports traceability verification.
