Discrete Dynamics in Nature and Society

Volume 2016, Article ID 3263587, 15 pages

http://dx.doi.org/10.1155/2016/3263587

## A Reliable Image Watermarking Scheme Based on Redistributed Image Normalization and SVD

^{1}Department of Natural and Applied Science, Glocal University, Saharanpur-247122, India^{2}Department of Computer Engineering, Sungkyunkwan University, Suwon-440746, Republic of Korea^{3}Department of Applied Science and Engineering, IIT Roorkee-247667, India^{4}Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi, EA 3956), Université Paris-Est Créteil Val de Marne, 61 avenue du Général de Gaulle, 94010 Créteil, France

Received 23 July 2015; Revised 4 October 2015; Accepted 15 October 2015

Academic Editor: David Arroyo

Copyright © 2016 Musrrat Ali 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

Digital image watermarking is the process of concealing secret information in a digital image for protecting its rightful ownership. Most of the existing block based singular value decomposition (SVD) digital watermarking schemes are not robust to geometric distortions, such as rotation in an integer multiple of ninety degree and image flipping, which change the locations of the pixels but don’t make any changes to the pixel’s intensity of the image. Also, the schemes have used a constant scaling factor to give the same weightage to the coefficients of different magnitudes that results in visible distortion in some regions of the watermarked image. Therefore, to overcome the problems mentioned here, this paper proposes a novel image watermarking scheme by incorporating the concepts of redistributed image normalization and variable scaling factor depending on the coefficient’s magnitude to be embedded. Furthermore, to enhance the security and robustness the watermark is shuffled by using the piecewise linear chaotic map before the embedding. To investigate the robustness of the scheme several attacks are applied to seriously distort the watermarked image. Empirical analysis of the results has demonstrated the efficiency of the proposed scheme.

#### 1. Introduction

Digital watermarking [1, 2] is the process of insertion of digital watermark in media content and its extraction, if required, for authentication or ownership verification of media content. A digital watermark is a piece of information that is hidden directly in media content, in such a way that it is imperceptible to a human observer but easily detected by a computer [3]. Different types of digital watermarking methods for digital contents have been developed that are classified into different categories depending upon the use and the requirement of information required for the extraction/detection of watermark. To check the authenticity of a digital content fragile watermarking is used while, for the purpose of copyright protection, robust watermarking is utilized. This classification is application-dependent. Based on the information required for the extraction/detection process watermarking schemes can be classified into blind, semiblind, and nonblind categories. Also, one more categorization is possible depending upon the domain of embedding of watermark: spatial and frequency. A detailed review of watermarking schemes can be found in [4, 5].

In a robust image watermarking scheme, a trade-off always exists among the two conflicting objectives, imperceptibility (also known as perceptual transparency) and robustness. So, the main goal of a robust image watermarking scheme is to produce the watermarked image with low quality degradation and high robustness. Increasing the amount of embedding information in an image may enhance its robustness to intentional or unintentional distortions applied to the image while simultaneously scarifying its imperceptibility and vice versa. Therefore, in order to improve these objectives, researchers have proposed several watermarking schemes implemented in spatial as well as transformed domain that find a compromise between these two objectives. The spatial domain watermarking techniques directly embed the watermark into the host image by altering the pixel values [6–9]. These methods generally are less robust to image and signal processing attacks and required low computational efforts, while frequency domain methods transform the representation of spatial domain into the frequency domain and then modify its frequency coefficients to embed the watermark. There are many transform domain watermarking techniques such as discrete cosine transforms (DCT) [10, 11], discrete Fourier transforms (DFT) [12–14], discrete wavelet transforms (DWT) [15–17], and singular value decomposition (SVD) [2, 18–20]. These methods typically provide higher image imperceptibility and are much more robust to image manipulations, but the computational cost is higher than spatial domain watermarking methods. The performance of watermarking methods was further improved by combining two or more transformations [21–36]. The idea was based on the fact that combined transforms could compensate for the drawbacks of each other, resulting in effective watermarking.

The singular value decomposition (SVD) is extensively used in image watermarking field in recent years due to its features. However, various researchers pointed out the false positive detection problem in most of the SVD-based algorithms [33, 37–40]. To counter this problem, numerous researchers have proposed improved versions of SVD-based image watermarking schemes. A robust image watermarking scheme based on SVD that embeds the entire watermark is given in [19]. There are two versions of this scheme depending on the implementation of SVD, to entire cover image and block-wise. The imperceptibility of an image watermarking scheme using block based SVD proposed in [18] is improved by incorporating compensation operation. According to this scheme, the damage in the quality due to insertion of the watermark in the left singular vector matrix is compensated by modifying the right singular vector matrix. The host image is segmented into nonoverlapping blocks of size 4 × 4; then the embedding blocks are selected at random. The watermark bits are embedded by modifying the coefficients in the first column of the left singular vector matrix of the target blocks. The different regions within an image have different local features, so some visual models such as human visual system (HVS) may be incorporated in finding the suitable embedding regions to improve robustness while maintaining imperceptibility. Based on this concept, a blind SVD-based watermarking scheme is presented in [34]. The host image is segmented into nonoverlapping blocks of size 8 × 8; then the embedding blocks are selected based on the sum of visual and edge entropies. The watermark bits are embedded by modifying the coefficients in the first column of the left singular vector matrix of the target blocks. The above mentioned SVD-based watermarking schemes embed the entire watermark within the cover image. It has improved the reliability of the watermarking but sacrificed the transparency. Also, these schemes are applicable only for the black and white watermark. If the watermark image is grayscale, then encode it to binary before embedding. A watermarking scheme proposed in [41] is based on the fact that SVD subspace (left and right singular vectors) can preserve a significant amount of information about an image. Therefore, it embeds the principal component, multiplication of left singular vector matrix and the singular value matrix, of watermark into the host image instead of singular values of the watermark. On the same concept, Run et al. [36] introduced an image watermarking scheme embedding the principal component of the watermark in frequency domain (DCT and DWT domains, resp.). Also, an optimization technique is applied to get the optimal scaling factors for embedding. Guo and Prasetyo [33] have extended this principal component concept to the block based watermarking. For convenience, this “false positive free SVD-based watermarking” scheme is abbreviated as “*FPF-SVD-W*” and used throughout the paper. The low frequency subband of the DWT transformed image is segmented into nonoverlapping blocks equal to the size of the watermark. Then the largest singular value of each block is modified by adding to it a scalar multiple of the corresponding element of the principal component matrix of the watermark. This reliable SVD-based watermarking scheme has solved the false positive detection problem and shown the resistance against some image manipulation attacks. It is not robust against some special distortions, such as image rotation in integer multiple of ninety degrees and image flipping (rows/columns). It is also realized from this scheme that a single scaling factor is not suitable to scale all the elements of the principal component matrix of the watermark. As the largest singular values from each block of the image have a different tolerance limit of modification to embed the watermark, it results in some visible distortions in the watermarked image with single scaling factor.

The focus of this research is on imperceptibility and robustness improvement of the reliable SVD-based image watermarking* FPF-SVD-W*. This paper proposes a novel and efficient and reliable SVD-based image watermarking scheme by incorporating the redistributed image normalization and variable scaling factor. To make the proposed scheme resilient against the attacks, such as rotation in an integer multiple of ninety degrees and image flipping, which change the locations of the pixels but do not make any changes to the pixel’s intensity of the image, redistributed image normalization is utilized. To mitigate the problem of visible distortions in the watermarked image, a variable scaling factor in employed depending on the coefficient’s magnitude to be embedded. In the proposed scheme the host image is redistributed and then some normalization operators are applied. Subsequently, the low frequency subband of the DWT transformed image is segmented into nonoverlapping blocks and SVD is applied to each block to get the largest singular value. The watermark is shuffled by using the piecewise linear chaotic map [35, 42] and then the elements of the principal component matrix of the watermark are embedded into the blocks by modifying the largest singular value of the corresponding blocks. The performance of the proposed scheme has been analyzed using several cover and watermark images and fifteen distortion attacks. Experimental results indicate that the proposed scheme not only gives the best results, but also outperforms the other SVD-based scheme* FPF-SVD-W*.

The rest of the paper is organized as follows. A brief review of the components of the proposed scheme is given in Section 2. The watermarking scheme* FPF-SVD-W* is discussed in detail in Section 3. Section 4 describes the proposed scheme. The experimental results are analyzed in Section 5. Finally, Section 6 draws the conclusions based on this research.

#### 2. Brief Review of Components of the Proposed Scheme

##### 2.1. Piecewise Linear Chaotic Map (PWLCM)

To enhance the confidentiality of the watermarking scheme, encryption of the watermark must be done before embedding into cover image. There are several chaotic maps that can be used for encryption; piecewise linear chaotic map (PWLCM) [35, 42] is one of them that recently has gained popularity due to its simplicity in representation and efficiency in implementation, as well as good dynamical behavior. Since the chaotic signal generally has good invariance to disturbance due to the low correlation between the initial parameters, it has been widely utilized for encryption and data hiding applications. The PWLCM, mathematically, is described inwhere is control parameter and considered as secret key. The most attractive features of PWLCM are its extreme sensitivity to initial conditions and the outspreading of orbits over the entire space. It is a noninvertible transformation of unit interval onto itself.

The encryption process of an image () of size based on this map is performed by implementing the steps given in Algorithm 1. An illustration of output of Algorithm 1 is given in Figure 1, by considering the different cases.