Security and Communication Networks

Volume 2018, Article ID 6712065, 17 pages

https://doi.org/10.1155/2018/6712065

## A Novel Blind and Robust Video Watermarking Technique in Fast Motion Frames Based on SVD and MR-SVD

^{1}LCCNS Laboratory, Department of Electronics, Faculty of Technology, Ferhat Abbas Setif University, Algeria^{2}LIS Laboratory, Department of Electronics, Faculty of Technology, Ferhat Abbas Setif University, Algeria

Correspondence should be addressed to Imen Nouioua; zd.fites-vinu@enemi_auoiuon

Received 23 July 2018; Revised 6 October 2018; Accepted 14 October 2018; Published 13 November 2018

Academic Editor: David Megias

Copyright © 2018 Imen Nouioua et al. 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.

#### Abstract

In this work, a novel and efficient digital video watermarking technique based on the Singular Value Decomposition performed in the Multiresolution Singular Value Decomposition domain is proposed. While most of the existing watermarking schemes embed the watermark in all the video frames, which is time-consuming and also affects the perceptibly of the video quality, the proposed method chooses only the fast motion frames in each shot to host the watermark. In doing so, the number of frames to be processed is consequently reduced and a better quality of the watermarked video is also ensured since the human visual system cannot notice the variations in fast moving regions. The watermark information is embedded by Quantization Index Modulation which is a blind watermarking algorithm. The experimental results demonstrate that the proposed method can achieve a very good transparency, while being robust against various kinds of attacks such as filtering, noising, compression, and frame collusion. Compared with several methods found in the literature, the proposed method gives a better robustness.

#### 1. Introduction

In recent years, the fabulous growth of the internet technology and the expansion of powerful computing devices have not only boosted the multimedia electronic commerce up but also incited artists to share and promote their work online. This obviously implied a massive presence on the web of digital multimedia data such as audio, image, and video. However, with the spread-out and ease of use of powerful multimedia dedicated processing tools, these data can be downloaded, easily modified, illicitly appropriated, and then largely redistributed or commercialized on the Internet. Protecting intellectual property rights of owners has then become a major concern. A solution to this problem is provided by digital watermarking [1].

A secure digital watermarking technique comprises two procedures: an embedding procedure and an extraction procedure performed by the use of embedding and extraction keys. The embedding procedure consists of inserting in the host multimedia content (usually called the cover) a watermark which is a digital signature that holds copyright information exclusively limited to the owner. Consequently, by means of the given secret keys, the extraction procedure permits solely to the owner or to an authorized recipient of the digital content to retrieve the watermark from the watermarked content [2].

In an efficient watermarking process, two important properties have to be taken into account [3]: imperceptibility: for an invisible watermarking scheme there must be no discernible difference between the original and the watermarked contents and robustness: the embedded watermark should be able to survive, to some extent, intentional and unintentional content manipulations.

Digital watermarking systems work either in the spatial domain or in a transform domain. A spatial domain technique works directly on pixels: the watermark is embedded by usually modifying directly the pixels values such as least significant bits (LSBs) [4], whereas a transform domain technique embeds the watermark by adjusting the transform domain coefficients. Popular transforms that have been frequently used are the Discrete Cosine Transform (DCT) [5], the Discrete Wavelet Transform (DWT) [6], the Discrete Fourier Transform (DFT) [4], and the Singular Value Decomposition (SVD) [7, 8]. Many combinations between these transforms have also been investigated in the literature to accomplish better results [9, 10]. Compared to spatial domain techniques, transform domain ones have been shown to achieve better robustness and imperceptibility [9]. Furthermore, according to the watermark extracting process, digital watermarking systems are categorized in three schemes [2]: blind, semi-blind, and non-blind. In a blind watermarking scheme, neither the original cover nor the embedded watermarks are required for detection but just the secret keys [2, 7, 11]. In a semi-blind watermarking scheme, only some information from the original cover and the secret keys are needed [2, 12]. A non-blind watermarking scheme requires the original cover, the original watermark, and the secret keys [2, 9]. This makes the blind watermarking schemes the most challenging ones to develop.

Initially, digital watermarking has been mainly studied for still images but in recent few years a considerable number of techniques dealing with video watermarking have been considered. However, one must say that video watermarking algorithms are more difficult to develop than those operating on images. This is essentially due to the temporal dimension which necessitates some specific requirements [13]: The robustness of the watermark should deal not only with common image processing attacks such as noise adding and JPEG compression, but also with video processing attacks such as MPEG compression and frame synchronization attacks. The imperceptibility in video watermarking is more difficult to achieve due to motion of objects in video sequences, so the temporal dimension should be taken into account in order to avoid distortion between frames. The complexity of the watermarking scheme should be low because of the significant number of frames to be processed in a video signal. Given that a digital video sequence is considered basically as a collection of sequential images [14], many of the image watermarking techniques that are present in the literature were extended to video [6, 9, 15, 16], as they embed the watermark in all frames of the video sequences. Thus, these algorithms are robust to frame dropping and frame swapping, but in return they are time-consuming and also affect the perceptibly of the video quality. To solve this problem of frame by frame embedding an answer to the following key question should be found: What are the preferred frames to host the watermark without degrading the visual quality of the watermarked video while maintaining the robustness reasonably unaffected? The answer is to adaptively embed the watermark in selected frames. In this direction, very few video watermarking schemes were considered. Tabassum and Islam [17] proposed a digital video watermarking technique based on identical frame extraction. In this method, the host video is initially divided into video shots. Then from each video shot one video frame called identical frame is selected for watermark embedding. In [18], Agilandeeswari and Ganesan developed an approach for video watermarking using SVD and DWT. In their algorithm they extracted the non-motion frames from the video using histogram difference based scene change detection algorithm, and then they embedded in them the same watermark. However, the problem in these techniques is the small number of watermarked video frames. So if those embedded frames are lost, the scheme becomes unreliable.

Jiang Xuemei et al. [19] developed an approach for video watermarking based on shot segmentation and block classification. They selected the frames with the biggest luminance value in every shot to be the host frames. The watermark signal is cropped into small watermarks according to the number of host frames in the host video. These small watermarks are then, respectively, embedded into the different selected host frames. Also, Chetan et al. [20] proposed a robust video watermarking scheme based on scene changes which embed different parts of a single watermark into different scenes of a video. These frames are selected based on scene change detection. In these two last cited techniques, if one watermarked frame is lost, the watermark cannot be extracted completely.

In this work, we propose a novel video watermarking scheme in fast motion frames using Singular Value Decomposition in the Multiresolution Singular Value Decomposition (MR-SVD) domain. The main contribution of our work is as follows:(i)In order to avoid embedding the watermark in all the frames of the video sequences, we first segment the video into temporally stationary signals using shot boundary detection. Then, from each shot we choose the frames with big motion energy (fast motion frames) to embed the watermark. This is done because the human visual system (HVS) cannot notice the details of fast moving regions [21] and thus the perceptual invisibility of the watermark is guaranteed.(ii)Because of their relevant advantages, we use a combination of the SVD and MR-SVD transforms. SVD, with its attractive mathematical properties, has been broadly applied in image compression and image watermarking and proved to be an efficient technique in both domains [22]. Most existing SVD based watermarking techniques combine the SVD transform with the multiresolution 2D-DWT [9, 10], as they were shown to be reliable and provide high robustness and better perceptual image quality. However, one of the drawbacks of the DWT is its huge resources consummation and high computation cost due to the convolutions carried out in each of the filters. To overcome this issue, Kakarala and Ogunbona [23] proposed the idea of the MR-SVD which performs multiresolution decomposition similar to that of the dwt, has perfect reconstruction, and above all is a matrix based operation like the SVD. Therefore a hybrid SVD MR-SVD watermarking technique is based only on matrix operations which make it well suited for real-time applications and simple for hardware implementation.(iii)Also, we embed watermark information by Quantization Index Modulation (QIM) which has been shown to be host interference free and provably optimal in terms of channel capacity under an additive white Gaussian noise attack. Furthermore, the extraction procedure in QIM is blind which makes it suitable for robust watermarking [24].(iv)Moreover, to embed the watermark in a secure manner, we encrypt the watermark using a logistic map based encryption [25].

This paper is organized in five sections. The next one introduces the preliminaries of our scheme. Section 3 gives the details of the proposed video watermarking which include four parts: the fast motion frames extraction, the watermark preprocessing, and the watermark embedding and extracting processes. The experimental results concerning the transparency and robustness against various attacks with comparisons with other previous algorithms found in the literature are presented in Section 4. Finally, conclusions are given in the last section.

#### 2. Preliminaries

##### 2.1. Singular Value Decomposition

In linear algebra, Singular Value Decomposition (SVD) is a numerical technique that decomposes a matrix into three matrices with valuable properties when applied in digital image processing [22]. If a matrix A represents, for example, an image of size N × N, then the SVD of A is given bywhere U and V are orthogonal matrices representing, respectively, the horizontal and vertical details (edges) of the image and S is a diagonal matrix, where the diagonal elements with are the singular values (SVs) of A.

Two main properties, related to the SVs, make the SVD appropriate for watermarking when the matrix S is utilized [8, 22]:(i)The energy content (luminance) of the image A is located in the SVs.(ii)The SVs have very good stability; i.e., a small perturbation added (a watermark, for example) to the image does not change significantly the SVs.

##### 2.2. Multiresolution Singular Value Decomposition (MR-SVD)

As stated in the Introduction, the MR-SVD initially introduced in [23] is a matrix based operation.

###### 2.2.1. 1D Multiresolution Singular Value Decomposition

Let represent a finite extent 1D signal and assume that N is divisible by 2L for some . Let the data matrix at the first level, denoted by X1, be constructed with its top and bottom rows containing, respectively, the odd-numbered and even-numbered samples of X:The corresponding centred matrix is , where is the identity and is the vector containing all ones.

Let U1 be the eigenvector matrix bringing the scatter matrix into diagonal form:where contains the squares of the two singular values, with ≥ .

Now let .

The top row of *,* denoted by , corresponds to the largest eigenvalue and represents the approximation component. The bottom row of , designated by , corresponds to the smallest eigenvalue and contains the detail component. The successive levels of decomposition repeat the procedure described above by placing the approximation component in place of X. Hence the MR-SVD can be written aswhere L is the desired level of decomposition.

###### 2.2.2. 2D Multiresolution Singular Value Decomposition

We briefly describe here the 2D MR-SVD. The first-level decomposition of the image proceeds as follows. The M × N image X is divided into nonoverlapping blocks and each block is arranged into a vector by stacking columns to form the data matrix .The eigendecomposition of the scatter matrix is

LetThe top row of the resulting matrix is rearranged to form an matrix which is considered as the smooth (approximation) components of the image. The remaining rows , , and contain the detail components, which are denoted by , , , respectively. The complete transform can be represented as follows:The original image X can be reconstructed from the right hand side, since the steps are reversible. As an example, the one-level MR-SVD decomposition of the video frame “Foreman” is depicted in Figure 1.