Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Study of Wavelet Based Composite Digital Image Watermarking Techniques Gurdeep Kaur1, Vinay Bhatia2 Department of ECE, Baddi University of Emerging Sciences and Technology, Baddi, India gurdeepkaur15may@gmail.com, vinay4research@yahoo.com
Abstract- Protection of digital multimedia content has become an important issue for the content owners as well as service providers. Watermarking has evolved as an important technology to achieve the data hiding. This paper presents a study of composite image watermarking technique adopted by various researchers for data hiding over the Internet. An idea of fusing multiple watermark images using wavelet fusion algorithm has been studied through this paper. In this technique the resultant fused watermark obtained earlier is embedded in the original image using a composite DWT-SVD (Discrete Wavelet Transform-singular value decomposition) watermarking algorithm to produce the watermarked image and the information can be extracted later or detected for a various purposes which include copy prevention and proof authentication. The performance of the algorithm is studied, the image details are found and a comparative study is done between the composite DWT-SVD and DWT watermarking algorithm for single and multiple watermarks. Thus Digital watermarking has been studied as a key solution in securing data transfer from illegal interferences Keywords- Composite Digital Watermarking; Data Hiding; Discrete Wavelet Transform (DWT); Singular Value Decomposition (SVD); Robustness; Wavelet Fusion.
I.INTRODUCTION Today’s world is a digital world. Nowadays, in almost every field of communication and multimedia there is a wide use of digital contents. Generally, the Information on internet and multimedia networks has all gone digital. Since this information is very easy to copy, distribute and modify; the content of this information can be easily destroyed by some intruders. Therefore, there is great need to monitor such illegal copy of digital media. Digital watermarking can serve as an effective technique in this area. In this technique information known as watermark is embedded into an image or contents, which can be later extracted or detected for variety of purposes including identification and authentication purposes [1]. Beyond the copyright protection, digital watermarking is having some other applications such as fingerprinting, content authentication, security, medical application, owner identification etc. Digital image watermarking can be performed in two domains viz. spatial domain and frequency domain. In spatial domain watermarking, pixel values of image are directly changed in order to hide the secret information. In frequency domain watermarking, image is first converted into frequency components and then these frequency components are used to hide the secret information. Though spatial domain watermarking is easy to perform, it is vulnerable to malicious attacks and hence it is less robust than frequency domain watermarking. In contrast, frequency domain watermarking is more robust at the cost of its computational complexity. In recent years, a good BUEST, Baddi
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amount of work has been carried out in frequency domain watermarking using transforms like DCT (Discrete cosine transform), DWT, DFT and SVD. There is one more technique that is Composite DWT- SVD which is gaining much more popularity than any other techniques [4]. Following are some characteristics of a good watermarking technique [1]-[3]: 1. Robustness: This characteristic refers to the property by which the watermarked image should not be removed by unauthorized person, thus it should resist modifications by attacks. 2. Capacity: This property describes that how much data should be embedded as a watermark to successfully detect during extraction. Different application has different capacity requirements. 3. Security: Security refers to protection of embedded watermark against various attacks which try to remove the watermark. 4. Imperceptibility: Imperceptibility refers to the perceptual similarity between host image and watermarked Image. 5. Fidelity: The watermark should not be noticeable to the viewer nor should the watermark degrade the quality of the content. This characteristic is called Fidelity. Although several algorithms have been developed for digital image watermarking to achieve data hiding, a problem that still arises is that the quality and the robustness of the watermarked image is decreased as we increase the capacity of the watermark information. So the main objective of developing a digital image watermarking technique using DWT-SVD (Discrete Wavelet transform and Singular Value Decomposition) is to improve the robustness and capacity of quality of embedded information. This paper presents multiple watermark images which are first fused using wavelet fusion. Then, the resultant fused watermark is embedded in the original image using the composite DWT-SVD watermarking algorithm [1]-[2]. The rest of the paper is organized as follows: Section II provides the information regarding Digital Watermarking Process. Section III introduces overview of techniques used in composite digital watermarking while the proposed technique is described in Section IV. Section V describes the comparison between DWT and DWT-SVD Techniques and the last section VI presents the paper conclusion. II. DIGITAL WATERMARKING TECHNIQUE
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An overview of Digital Watermarking is shown in figure 1. A watermarking system is usually divided into three steps [1]: 1. Embed Process: An algorithm accepts the host and the data to be embedded and produces a watermarked signal. Then the watermarked digital signal is transmitted or stored. 2. Modify/Copy/Replace Process: Unauthorized person shall try to modify/Copy or may replace the content.
Figure.2.Discrete Wavelet Transform Process
The next step is obtaining an approximation image by reversing the process (figure 3).
Figure.1.Digital Watermarking Process 3. Extraction Process: Using this process we can extract the watermark. If the signal was unmodified during transmission, then the watermark still is present and it may be extracted.
III.OVERVIEW OF DIFFERENT TECHNIQUES USED IN DIGITAL WATERMARKING A. Discrete Wavelet Transform (DWT) In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, key advantage of the transform is its temporal resolution i.e. it may capture both frequency and locational information (location within time). DWT is implemented as a multistage transformation in which level wise decomposition is performed [5]. At level 1: Image is decomposed into four sub bands: LL (Approximation Detail), LH (Vertical Detail), HL (Horizontal Detail), and HH (Diagonal Detail) where LL denotes the coarse level coefficient which is the low frequency part of the image. LH, HL, and HH denote the finest scale wavelet coefficient. The LL sub band can be decomposed further to obtain higher level of decomposition. This decomposition can continues until the desired level of decomposition is achieved for the application. The watermark can also be embedded in the remaining three sub bands to maintain the quality of image as the LL sub band is more sensitive to human eye [6]. In figure 2 we show that how can original image is decomposed into LL, LH, HL, HH sub bands using DWT Technique.
Figure.3.DWT Extraction process Algorithm for carrying out DWT: Step1. Original image is decomposed into various subbands using DWT. In this paper one-level DWT is used that is shown in Figure 4.
LL
LH
HL
HH
Figure.4.One Level DWT Step2. Sub-band suitable for embedding watermark is chosen. Step3. Wavelet coefficients of the selected sub-band are modified according to the image to be watermarked. Step4. After embedding watermark, we obtain watermarked image. Advantages of DWT are: Wavelet is superior in quality in most situations; so it has an advantage over Fourier transform that it represents both frequency and time. Fourier transform shows only those frequencies that are used but will not show the frequencies that occur. DWT shows higher flexibility so that Wavelet function can be freely chosen. Some of the disadvantages of DWT are: Still show ringing artefacts, take longer compression time [8]-[9]. B. Composite DWT-SVD Technique
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Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Composite technique is a fusion of two techniques: DWT and SVD, they are used together which make their fusion very attractive watermarking technique. Before studying DWT-SVD Algorithm, SVD (singular value decomposition) watermarking technique can be studied. [9]. Singular Value Decomposition transform is a linear algebra transform which is used for factorization of a real or complex matrix with numerous applications in various fields of image processing. As a digital image can be represented in a matrix form with its entries giving the intensity value of each pixel in the image, SVD of an image M with dimensions m x m is given by: M = USVT Where, U and V are orthogonal matrices and S known as singular matrix is a diagonal matrix carrying non-negative singular values of matrix M.
1. In the first stage, the primary watermark is fused with the secondary watermark using wavelet fusion to produce fused watermark. Primary and secondary watermarks are the images generated by signals generated from different sensors. 2. In the second stage, the fused watermark is embedded in the original image using the composite DWT-SVD watermarking algorithm.
Figure.6. Proposed Technique
And
U TU = V TV = I
The columns of U and V are called left and right singular vectors of M, respectively. They basically specify the geometrical details of the original image. Left singular matrix i.e., U represents the horizontal details and right singular matrix i.e., V represents the vertical details of the original image. The diagonal values of matrix S are arranged in decreasing order which signifies that importance of the entries is decreasing from first singular value for the last one this feature is employed in SVD based compression techniques [10]. There are two main properties of SVD to employ in digital watermarking schemes: 1. Small variations in singular values do not affect the quality of image. 2. Singular values of an image have high stability so they do not change after various attacks. In Composite DWT-SVD watermarking Technique first using DWT we can decompose the image into four sub bands, then SVD is applied on the selected band. A complete algorithm is presented in proposed technique. IV. PROPOSED TECHNIQUE We are using Wavelet Based Image Fusion in which two or more images are combined into a single image, called fused image as shown in Figure 5. The fused image retains the most important features (desirable information). Then the technique follows as:
Figure.5. Fusion of Images The proposed Composite image watermarking technique consists of two stages as also shown in figure 6: BUEST, Baddi
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Following are the steps which are carried out in the proposed technique using DWT-SVD water marking: Step1. Using DWT the fused image is decomposed into various sub-bands depending on the level of decomposition used, here one-level decomposition is used. Step2. Choose a sub-band in which desired watermark is to be embedded keeping in mind various properties of the sub-bands. Step3. Apply SVD in the selected sub-band. Step4. Modify the singular values of that particular subband with respect to the watermark image Step5. After modification, inverse DWT of the image is taken which in result will give the watermarked image. On the other hand, the extraction process occurs in two steps: the extraction of fused image and the extraction of both the primary and the secondary watermarks [1].
V. COMPARISON BETWEEN DWT AND DWT-SVD TECHNIQUES As DWT and DWT-SVD both techniques are used for Digital Watermarking, comparison is done in a way that in case of DWT, decomposition of the original image is done to embed the watermark and in case of composite DWTSVD firstly image is decomposed according to DWT and then watermark is embedded in singular values obtained by applying SVD[7]. By using DWT-SVD technique we can improve the quality of the capacity of the embedded information and robustness without affecting the perceptual quality of the original image better than DWT Technique [3]. DWT-SVD technique is also preferable over DWT technique to hide watermarking information in noisy regions and edges of images, rather than in smoother regions to some extent [2]. On comparing the values of PSNR (Peak Signal to Noise Ratio) at different values of scaling factor, it is concluded that composite technique DWT SVD is much better than DWT technique [4]. Well both these techniques are very much robust and imperceptible. 298
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A. DWT TECHNIQUE MATLAB RESULTS The proposed DWT watermarking algorithm is simulated using MATLAB R 2007 b. This algorithm is tested for the various host and watermark images. Here are some results shown in which the image is decomposed using DWT watermarking technique. From where we obtain the Approximation, Vertical, Horizontal and Diagonal details of the host image.
Figure.7. d. Horizontal Details
Figure.7.DWT Detail Images a. Original Image (tt.jpg)
Figure.7. e. Diagonal Details Figure.7.b. Approximation detail
Now according to purposed technique, one of the subbands is chosen and watermarking is performed. VI. CONCLUSION
Figure.7.c.Vertical Details 299
A composite digital image watermarking technique is proposed and studied. In the proposed technique, two watermark images are first fused then the fused watermark is embedded using the DWT–SVD watermarking algorithm. And Composite DWT-SVD watermarking algorithm is suitable for the extraction of the fused watermark even in the presence of attacks. By using DWTSVD we can improve the quality of the capacity of the embedded information and robustness without affecting the perceptual quality of the original image. In this paper we also have presented a review of the significant techniques in existence for watermarking those which are employed in copyright protection. Along with these, an introduction to digital watermarking has been presented.
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Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
This paper shows the different techniques used in digital watermarking and the experimental results using DWT Technique are also shown. In future we wish to work on the proposed algorithm of DWT-SVD watermarking technique defined in this paper and that can be implemented using Matlab. REFERENCES [1] Ezz El-Din Hemdan1, Nawal El-Fishaw, Gamal Attiya1, Fathi Abd El-Samii, “Hybrid Digital Image Watermarking Technique for Data Hiding”, 30th National Radio Science Conference National Telecommunication Institute, Egypt, April 16-18, 2013. [2] Vipula Singh, "Digital watermarking: A tutorial," Cyber Journals, Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), January Edition,2011. [3] P. Bao and X. Ma "Image adaptive watermarking using wavelet domain singular value decomposition", IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 1, pp.96 -102, 2005. [4] Morteza Makhloghi, Fardin A. Tab, and Habibollah Danyali, "A new robust blind DWT-S VD based digital image watermarking," IEEE International Conference on Emerging Trends in Electrical and Computer Technology (lCETECT), 2011. [5] Bhupendra Ram, “Digital Image Watermarking Technique Using Discrete Wavelet Transform and Discrete Cosine Transform” International Journal of Advancements in Research & Technology, Volume 2, Issue4, April2013. [6] C. C. Lai and C. C. Tsai, Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition”, IEEE Trans. on Instrumentation and Measurement, vol. 59, no. 11 pp. 3060-3063 2010. [7] Nidhi Bisla, Prachi Chaudhary, “Comparative Study of DWT and DWT-SVD Image Watermarking Techniques”, Volume 3, Issue 6, June 2013, International Journal of Advanced Research in Computer Science and Software Engineering, Available online at: www.ijarcsse.com. [8] Mohsen Kariman Khorasani, Mohammad Mojtaba Sheikholeslami , “An DWT-SVD Based Digital Image Watermarking Using a Novel Wavelet Analysis Function ”, Fourth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 242-246, 2012. [9] Seema and Sharma Sheetal,“DWT-SVD Based Efficient Image Watermarking Algorithm to Achieve High Robustness and Perceptual Quality”, International Journal of Advanced Research in Computer Science and Software Engineering,vol. 2, Issue 4, 2012. [10] R. liu and T. tan, "An SVD-Based Watermarking Scheme for protecting rightful ownership," IEEE Trans. on multimedia, Vol. 4, No. 1 March 2002.
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