  DNNs shall be considered as the intellectual property (IP) of the model
builder due to the impeding cost of designing/training a highly accurate model.
Research attempts have been made to protect the authorship of the trained model
and prevent IP infringement using DNN watermarking techniques. In this paper,
we provide a comprehensive performance comparison of the state-of-the-art DNN
watermarking methodologies according to the essential requisites for an
effective watermarking technique. We identify the pros and cons of each scheme
and provide insights into the underlying rationale. Empirical results
corroborate that DeepSigns framework proposed in [4] has the best overall
performance in terms of the evaluation metrics. Our comparison facilitates the
development of pending watermarking approaches and enables the model owner to
deploy the watermarking scheme that satisfying her requirements.
