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
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: firstname.lastname@example.org, email@example.com).
Nguyen Minh Hoang is with the Faculty of Management Information Systems, Banking University of HCM City, Vietnam (e-mail: firstname.lastname@example.org).
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).