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IJMLC 2020 Vol.10(5): 675-684 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.5.990

An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

Nguyen Chi Sy, Ha Hoang Kha, and Nguyen Minh Hoang

Abstract—Digital watermarking is one of the most widely used techniques for the protection of ownership rights of digital audio, images, and videos. One of the desirable properties of a digital watermarking scheme is its robustness against attacks aiming at removing or destroying the watermark from the host data. Different from the common watermarking techniques based on the spatial domain or transform domain, in this paper, a novel scheme of digital image blind watermarking based on the combination of the discrete wavelet transform (DWT) and the convolutional neural network (CNN) is proposed. Firstly, the host images are decomposed by the DWT with 4 levels and, then, the low frequency sub-bands of the first level and the high frequency sub-bands of the fourth level are used as the input data and the output target data to train the CNN model for embedding and extracting the watermark. Experimental results show that the proposed scheme has superior performance against common attacks of JPEG compression, mean and median filtering, salt and pepper noise, Gaussian noise, speckle noise, brightness modification, scaling, cropping, rotation, and shearing operations.

Index Terms—Terms—Robust image watermarking, discrete wavelet transform, convolutional neural network, copyright protection.

Nguyen Chi Sy and Ha Hoang Kha are with the Faculty of Electrical & Electronics Engineering, Ho Chi Minh City University of Technology, VNU-HCM, Vietnam (e-mail: chisy.nguyen@gmail.com, hhkha@hcmut.edu.vn).
Nguyen Minh Hoang is with the Faculty of Management Information Systems, Banking University of HCM City, Vietnam (e-mail: nmhoang@gmail.com).

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Cite: Nguyen Chi Sy, Ha Hoang Kha, and Nguyen Minh Hoang, "An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 675-684, 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).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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