Abstract—Recently in the vast advancement of Artificial
Intelligence, Machine learning and Deep Neural Network (DNN)
driven us to the robust applications. Such as Image processing,
speech recognition, and natural language processing, DNN
Algorithms has succeeded in many drawbacks; especially the
trained DNN models have made easy to the researchers to
produce state-of-art results. However, sharing these trained
models are always a challenging task, i.e. security, and
protection. We performed extensive experiments to present
some analysis of watermark in DNN. We proposed a DNN
model for Digital watermarking which investigate the
intellectual property of Deep Neural Network, Embedding
watermarks, and owner verification. This model can generate
the watermarks to deal with possible attacks (fine-tuning and
train to embed). This approach is tested on the standard dataset.
Hence this model is robust to above counter-watermark attacks.
Our model accurately and instantly verifies the ownership of all
the remotely expanded deep learning models without affecting
the model accuracy for standard information data.
Index Terms—Watermark, embedded, ownership verification, deep neural network.
Farah Deeba and She Kun are with the School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China (e-mail: firstname.lastname@example.org, Kun@ uestc.edu.cn).
Fayaz Ali Dharejo is with the Computer Network Information Center, Chinese Academy of Sciences University of Chinese Academy of Sciences, Beijing, Haidian 100190, China (e-mail: email@example.com).
Hmaeer Lnagah and Hira memon are with the Computer System Engineering, Quiad e Awam University of Engineering Science and Technology of Nawabshah, 67450, Pakistan (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Farah Deeba, She Kun, Fayaz Ali Dharejo, Hameer Langah, and Hira Memon, "Digital Watermarking Using Deep Neural Network," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 277-282, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).