International Journal of Engineering & Science Research IMPROVED DWT-SVD BASED COLOR IMAGE WATERMARKING Priya tomar* 1, Annu Malik 1, Pramod Vishwakarma 1, Dr. Sanjay Singh 2 1 Assistant Professor, Department of Computer Science and Engineering, REMTECH, Shamli, India. 2 Assosiate Professor, Department of Electronics & Communication Engineering, Uttaranchal University, ABSTRACT Dehradun, India. In this paper we present Discrete Wavelet Transform (DWT) and Single Value Decomposition (SVD) hybrid robust watermarking techniques. Security of digital multimedia content has become an important issue to concern. In order to protect digital multimedia content. Watermarking techniques are used. Secret multimedia data can be embedded in spatial domain or transform domain. In this paper we use transform domain for more robustness. Keywords: Discrete wavelet transform (DWT), Singular value decomposition (SVD), Copyright protection, Multifrequency Image. 1. INTRODUCTION An essential aspect that affects the multimedia networked services is that the multimedia content is distributed over the network.publishers, authors and creators of these content allow the distribution of multimedia data over the network. There are many tools that are available to duplicate and change the digital content. Ease of data reproducing in their exact original form violating copyright protection.hence, creaters of multimedia digital content searching various approaches for copyright protection of their multimedia data. In digital watermarking, a pattern of bits introduce into a digital image, audio signal, video signal or text file that identifies the file's copyright information. Image Watermarking is the practice of embedding a trifling signal into a picture such that the signal can be detected or extracted later to create an assertion pertaining to the image. In general, any watermarking system consists of the following three parts: The Watermark signal Watermark embedder that embeds the watermark. Watermark detector that verifies the existence of watermark. Most current image watermarking research focuses on indiscernible watermarks, those which are indistinguishable under ordinary viewing conditions. According to the location of watermark embedded, digital watermarking algorithm can be divided into spatial and transform domain algorithms. Algorithms based on frequency domain are to embed watermark into the data after all kinds of image transformation. In frequency domain, energy distribution is centralized which helps to ensure the invisibility of the watermark [6].Therefore, most of the current watermarking algorithms are achieved in the frequency domain. The frequency domain is also called the transform domain. We can embed watermark in Discrete Cosine transform (DCT), Discrete wavelet Transform (DWT) domains etc. The most important strength offered by transform domain techniques is that they can take advantage of properties of updating domains to address the restrictions of pixel-based methods or to support additional features. Digital watermarking systems can be divided in three schemes depending upon the information need for extraction: (i) Blind watermarking (ii) Semi-Blind watermarking *Corresponding Author www.ijesr.org 109
(iii) Non Blind watermarking Blind watermarking (Private watermarking): Uses Secret key for extraction of information. Semi-Blind watermarking: It uses watermark bit sequence and secret key for extraction of Information. Non Blind watermarking (Public Watermarking): It needed original image, secret key and watermark for extraction of information. Recently, a transform called singular value decomposition was explored. In this paper, we present a hybrid SVD-DWT semi blind approach to embed the visual watermark in high frequency band of the image. 2. BACKGROUND AND THEORY In this paper we are giving a new image watermarking technique. This technique increases the imperceptibility and robustness of watermark. To evaluate the performance of our watermark we use the model of Peak-tosignal-noise-ratio (PSNR) and Pearson s correlation coefficient. Means the difference between the original image and the attacked watermarked image is being calculated.it is a semi-blind watermarking method. Means original image is not required at the time of watermark recovery. 2.1 RGB Color Spaces Several of researchers have used RGB color space, based on RGB model.rgb are abbreviation for Red, Green and Blue respectively. R, G, B planes are divided using following equations : R =m(:,:,1) G =m(:, :, 2) B = m(:, :,3) (i) (ii) (iii) m is the name of image. We convert image into YUV color spaces which provide possibility of separating luminance and chrominance signals. 2.2 YUV Color Spaces Here, RGB color space is changed into YUV color space after converting it into YUV color space, Watermark is embedded using SVD and DWT method. Following equations are used for conversion: Y=0.299*R +0.587*G + 0.114*B U=-0.147*R -0.289*G + 0.436*B V=0.615*R-0.515*G -0.100*B (iv) (v) (vi) Using DWT and SVD watermark is embedded, later than, that YUV color space again converted into RGB color space using equations R = Y + 1.140 * V G = Y - 0.395 * U - 0.581 * V B = Y + 0.2032 * U (vii) (viii) (ix) For embedding the watermark we pertain frequency domain techniques, because the major gain of frequency domain methods is their better robustness to common image distortions. In multimedia applications, embedded watermarks should be undetectable, robust, and have a high capability. Invisibility refers to the degree of deformation introduced by the watermark and its distress the viewers or listeners. Robustness is the resistance Copyright 2013 Published by IJESR. All rights reserved 110
of an embedded watermark in opposition to intended attacks, such as noise, filtering (blurring, sharpening, etc.), re-sampling, scaling, rotation, cropping, and lossy compression. Capacity is the quantity of data that can be represented by an embedded watermark. Discrete cosine transform (DCT) and discrete wavelet transform (DWT), which are used in image compression standards JPEG and JPEG2000 respectively, are two main transform techniques used in transform domain watermarking. As DWT decomposes images into four bands, DWT- based watermarking methods can embed data in all frequencies. This result in robustness to a large range of attacks for embedding in small and high frequency bands is complementary. Several watermarking methods resistant to geometric attacks have been existing in literature. 2.3 Singular Value Decomposition (SVD) Recently watermarking schemes based on Singular Value decomposition (SVD) have gained eminence due to its ease in implementation and some remarkable mathematical features of SVD. Here a short description of SVD and its function in the watermarking schemes have been accessible. SVD is an effective numerical analysis tool used to evaluate matrices. In SVD transformation, a matrix can be decomposed into three matrices that are of the same size as the original matrix. Since the view point of linear algebra, an image is an array of nonnegative scalar entries that can be regarded as a matrix. Without trouncing simplification, if A is a square image, denoted as A R n n, where R symbolize the real number domain, then SVD of A is defined as.a= USV T Where U R n n and V R n n are orthogonal matrices and S R n n is a diagonal matrix, as Here diagonal elements i.e. σ s are singular values and satisfy. SVD is a best promising matrix decomposition method in a slightest square sense that it packs the maximum signal energy into as few coefficients as possible. It has the capability to amend the variations in local data of an image. SVD EXAMPLE: If SVD function is applied on this matrix A, then the matrix will be decomposed into equivalent three matrices as follows: Here diagonal fundamentals of matrix S are singular values and we notice that these values satisfy the non escalating order 77.9523 27.5619 1.3349 2.4 Discrete Wavelet Transform (DWT) DWT is a very vast topic. We will discuss basic concepts of DWT that are needed for this work. Detail of wavelets is given in [6]. Two frequently used abbreviations are DWT and IDWT.DWT includes Haar wavelet, Copyright 2013 Published by IJESR. All rights reserved 111
daubechies wavelet and others. DWT is Discrete Wavelet Transformation. It is the Transformation of sampled information, transformation of standards in a collection, into wavelet coefficients. IDWT is Inverse Discrete Wavelet Transformation: into the original sampled data. 3. PROPOSED MODEL 3.1 Watermark Embedding Algorithm 1. Take a color Image(M). 2. Separate RGB color from the image. R=M( :, :, 1); G=M(:, :, 2); B=M(:, :, 3); 3. Convert RGB color model into YUV color model:- Y=0.299*R+0.587*G +0.114 *B U=-0.147*R -0.289*G+0.436*B V=0.615*R-515*G-0.100*B 4. Decompose the Y channel using Haar DWT in $ubbands: [A H V D]=dwt[Y, Haar ]; 5. Apply IDWT to D and get highh frequency original Image(I h ). 6. SVD technique is applied to I h : [U h S h V h ] =SVD(I h ); 7. SVD technique is applied to Watermark: [U w S w V w ] =SVD(W) 8. Modify S h= S h + α *S w Where α denotes the scaling factor used to control the strength of watermark signal. 9. Obtain the modified high frequency image: I hiw =U*S iw *V T 10. Obtain the modified DWT coefficient i.e. [A H V D]= dwt((i hiw ), Haar )) 11. Obain the watermark image Aw by applying inverse DWT using one modified and other non modified coeffients: Aw = idwt [(A H V Dw), Haar ); 3.2 Watermark Extracting Algorithm 1. Take watermarked image Aw. 2. Separate RGB color using: R= Aw( :, :, 1) G=Aw( :, :, 2) B= Aw(:,:, 3) Workflow of DWT The inverse technique that converts wavelet coefficients Copyright 2013 Published by IJESR.. All rights reserved 112
3. Convert it into YUV model. 4. Apply haar DWT to decompose the watermarked Image. 5. Apply haar DWT to D and get modified image I hw. 6. Apply SVD to watermark Aw: [ Uhw Shw Vhw] =svd[i hw ]; 7. Apply SVD to watermark Aw [U ww S ww V ww ] =SVD[A w ]; 8. Compute : Swn = (S ww -S hw )/α; 9. Extracted watermark : W n =U ww *S wn *V ww T 4. PERFORMANCE EVALUATION 4.1 PSNR (The Peak Signal-to-Noise Ratio) The performance of the watermarking techniques can be measured by imperceptibility and robust capabilities. Imperceptibility means that the apparent quality of the original image should not be distorted by the occurrence of watermark. On the other hand, the robustness is the compute of the intentional and unintentional attacks. It was initiate that image is evaluated using peak-signal-to- noise-ratio (PSNR) and mean square error (MSE). The PSNR calculate the peak signal-to-noise ratio between two images in decibels. This ratio is frequently used as a eminence measurement between the original and a compressed image. The elevated the PSNR, the better the quality of the compressed, or reconstructed image. The Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR) are the two error metrics used to compare image compression quality. The MSE represents the cumulative squared error between the compressed and the original image, whereas PSNR represents a calculate of the peak error. The lower the value of MSE is, the lower the error. To compute the PSNR, the block first calculates the mean-squared error by means of the following equation: Where M and N are the number of rows and columns in the input images, respectively. Then the block computes the PSNR using the following equation: In the equation (5.11), R is the highest fluctuation in the input image data type. For example, if the input image has a double-precision floating-point data type, then R is 1. If it has an 8-bit unsigned integer data type, R is 255, etc. 4.2 Correlation Coefficient Pearson s correlation coefficient, r, is widely used in Pattern reorganization and image processing techniques. The correlation coefficient r has the value, r =1, if the two images are absolutely identical, r = 0 if the two images are not correlated, r = -1 if they are anti-correlated. It is used to compare the two images taken at different times. The r value indicates that image has been distorted or moved. In theory, we would obtain value of r=1 if the images are unharmed and r less than 1 if the images are distorted. In our experiment r lies between 0.7 to 0.8 which is closer to 1. Copyright 2013 Published by IJESR. All rights reserved 113
5. EXPERIMENT COVER IMAGE WATERMARK IMAGE WATERMA RKED IMAGE Fig 1 : Watermarked Image ATTACKS EXTRACTED WATERMARK PSNR CORREL-ATED COEFFIC-IENT ROTATION 30.14 0.7890 GAUSSIAN NOISE 29.35 0.8010 SALT AND PEPPER NOISE 31.55 0.7656 Copyright 2013 Published by IJESR. All rights reserved 114
6. CONCLUSION There are numerous types of algorithms for watermarking. Each type of algorithms has its own benefits and restrictions. No method can provide fully faultless elucidation. Each type of result has robustness to some type of attacks but is less flexible to some other types of attacks. Main focus of the present research in this field is to make the watermarking algorithms flexible to geometric transformations. In case of practical application, choice of elucidation type actuallyde- pends on the nature of application and necessities SVD based watermarking is relatively a new field and not that much work has beendone till now. Most of the SVD based algorithms are less flexible to geometric alteration including rotation, scaling etc. So incorporating robustness not in favor of some attacks in the SVD based algorithms is a recent tendency of research now. Here I have studied several SVD based algorithms and analyzed their relative advantages and limitations Some of the SVD based algorithms are pure SVD based whereas some others have used different types of transforms in order to enhance the robustness against different types of attacks. Among these transform and SVD based algorithms, I have developed here DWT based algorithm using SVD. In this paper we presents a semi-blind watermarking technique that uses DWT at level 2 and then embed watermark in high frequency band of the image. Proposed watermarking technique has following advantages: 1) In our scheme, the most difference from traditional scheme is that the watermarking is embedded in high frequency. It has good performance in a variety of image processing. 2) SVD decomposition belongs to spatial domain transform and has robustness to geometrical attack. For considering this, we use DWT and IDWT transformation to obtain the high frequency image. Accordingly the scheme has robustness to geometrical attack. 3) We notice there are three frequency image (low frequency image, middle-low frequency image, middle-high frequency image) are not used. Different watermarks can be embedded in them. 4) As it s a semi-blind scheme so when extraction is done there is no need for original cover image, only original watermark and the algorithm is required for detection of content ownership. 5) The PSNR value is between 20db-50db which shows that the extracted watermark from the attacked image is closer to the original watermark. REFERENCES [1] Hartung F, Kutter M. Multimedia Watermarking Techniques. Proc. of the IEEE 1999; 87(7): 1079-1107. [2] Mohanty SP. Watermarking of Digital Images. Submitted at Indian Institute of Science Bangalore, January 1999; 1.3-1.6. [3] Pik-Wah C. Digital Video Watermarking Techniques for Secure Multimedia Creation and Delivery. submitted at The Chinese University of Hong KongJuly 2004; 7-15. [4] Ganic E, Eskicioglu AM. Secure DWT-SVD Domain Image Watermarking: Embedding Data in All Frequencies. ACM Multimedia and Security Workshop, Magdeburg, Germany, September 20-21, 2004. [5] Kapre, Joshi. Robust Image Watermarking based on Singular Value Decomposition and Discrete Wavelet Transform, Nanded 2010 IEEE. [6] Zhang D, Sun W, Huang. A New Robust Watermarking Algorithm Based on DWT, Image and Signal processing 2009,CISP 09. [7] Sadek RA. Blind Synthesis Attack on SVD Based watermarking Technique. Computational Intelligence for modeling Control 2008 ; 140-145. [8] Abbate J. Inventing the Web. Proc. of the IEEE 1999; 87(11):1999 2002. [9] Sharma K.K. MATLAB Demystified. Vikas Publications, Edition I. [10] Swanson MD, Kobayashi M, Tewfik AH. Multimedia Data- Embedding and Watermarking Technologies. Copyright 2013 Published by IJESR. All rights reserved 115
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