  Deep learning techniques are one of the most significant elements of any
Artificial Intelligence (AI) services. Recently, these Machine Learning (ML)
methods, such as Deep Neural Networks (DNNs), presented exceptional achievement
in implementing human-level capabilities for various predicaments, such as
Natural Processing Language (NLP), voice recognition, and image processing,
etc. Training these models are expensive in terms of computational power and
the existence of enough labelled data. Thus, ML-based models such as DNNs
establish genuine business value and intellectual property (IP) for their
owners. Therefore the trained models need to be protected from any adversary
attacks such as illegal redistribution, reproducing, and derivation.
Watermarking can be considered as an effective technique for securing a DNN
model. However, so far, most of the watermarking algorithm focuses on
watermarking the DNN by adding noise to an image. To this end, we propose a
framework for watermarking a DNN model designed for a textual domain. The
watermark generation scheme provides a secure watermarking method by combining
Term Frequency (TF) and Inverse Document Frequency (IDF) of a particular word.
The proposed embedding procedure takes place in the model's training time,
making the watermark verification stage straightforward by sending the
watermarked document to the trained model. The experimental results show that
watermarked models have the same accuracy as the original ones. The proposed
framework accurately verifies the ownership of all surrogate models without
impairing the performance. The proposed algorithm is robust against well-known
attacks such as parameter pruning and brute force attack.
