Wmnet: A lossless watermarking technique using deep learning for medical image authentication

Yueh Peng Chen, Tzuo Yau Fan, Her Chang Chao*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

16 Scopus citations


Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be more objective. In this paper, we implemented a model based on deep learning technology that can accurately identify the watermark copyright, known as WMNet. In the past, when establishing deep learning models, a large amount of training data needed to be collected. While constructing WMNet, we implemented a simulated process to generate a large number of distorted watermarks, and then collected them to form a training dataset. However, not all watermarks in the training dataset could properly provide copyright information. Therefore, according to the set restrictions, we divided the watermarks in the training dataset into two categories; consequently, WMNet could learn and identify the copyright information that the watermarks contained, so as to assist in the copyright verification process. Even if the retrieved watermark information was incomplete, the copyright information it contained could still be interpreted objectively and accurately. The results show that the method proposed by this study is relatively effective.

Original languageEnglish
Article number932
JournalElectronics (Switzerland)
Issue number8
StatePublished - 02 04 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Convolutional neural network
  • Deep learning
  • Watermarking technique


Dive into the research topics of 'Wmnet: A lossless watermarking technique using deep learning for medical image authentication'. Together they form a unique fingerprint.

Cite this