A robust video zero-watermarking based on deep convolutional neural network and self-organizing map in polar complex exp
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A robust video zero-watermarking based on deep convolutional neural network and self-organizing map in polar complex exponential transform domain Yumei Gao 1 & Xiaobing Kang 1
& Yajun Chen
1
Received: 21 October 2019 / Revised: 9 August 2020 / Accepted: 16 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In this paper, a robust video zero-watermarking scheme for copyright protection using a combination of convolutional neural network (CNN) and self-organizing map (SOM) in polar complex exponential transform (PCET) space is presented. The scheme is developed not only to remedy the existing problems of lacking in some performance assessments but also to enhance the robustness. It starts with extracting the content feature of each frame by CNN and then some significant frames are selected using SOM clustering and maximum entropy. Secondly, the PCET is applied to all selected frames to abstract invariant moments, and further, is scrambled by a chaotic logistic map and is reduced in dimensions by singular value decomposition (SVD). Next, a binary sequence is generated by comparing adjacent values of the obtained compact PCET moments in the previous step, and further is permuted to produce a binary matrix. Finally, a bitwise exclusive-OR operation is imposed on the binary matrix and the encrypted watermark by the chaotic map to generate a zero-watermark signal. Experimental results demonstrate that the proposed scheme has adequate equalization and distinguishability of zero-watermarks as well as strong robustness against common signal processing, geometric, compression, and inter-frame attacks. Also, compared with existing video zero-watermarking and traditional video watermarking methods, the proposed scheme exhibits superior robustness. Keywords Zero-watermarking . Convolutional neural network . Self-organizing map . Polar complex exponential transform . Chaotic encryption
* Xiaobing Kang [email protected]
1
Department of Information Science, Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710048 Shaanxi, China
Multimedia Tools and Applications
1 Introduction With the arrival of the wireless mobile Internet era, digital videos have become the main carriers of information in people’s daily life gradually. However, the rapid development of network and multimedia technology has also posed many problems, among which copyright security has received the most attention. How to effectively secure these data has become a concern of researchers. As an effective way to protect intellectual property, digital watermarking [5, 11, 13, 28, 18] comes into researchers’ view. With the deepening of the usefulness of video in people’s life, the research on video watermarking should be paid more attention [1]. In the application of copyright protection and ownership identification for videos, robust blind watermarking has become the focus of research [1–3, 6, 14, 22]. A contourlet transform (CT) and principal component ana
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