  Protecting the Intellectual Property rights of DNN models is of primary
importance prior to their deployment. So far, the proposed methods either
necessitate changes to internal model parameters or the machine learning
pipeline, or they fail to meet both the security and robustness requirements.
This paper proposes a lightweight, robust, and secure black-box DNN
watermarking protocol that takes advantage of cryptographic one-way functions
as well as the injection of in-task key image-label pairs during the training
process. These pairs are later used to prove DNN model ownership during
testing. The main feature is that the value of the proof and its security are
measurable. The extensive experiments watermarking image classification models
for various datasets as well as exposing them to a variety of attacks, show
that it provides protection while maintaining an adequate level of security and
robustness.
