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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 Robust Color Image Watermarking Using Nonsubsampled Contourlet Transform C.Venkata Narasimhulu K.Satya Prasad Professor, Dept of ECE Professor, Dept of ECE, HIET, Hyderabad, India JNTU Kakinada, India narasimhulucv@gmail.com prasad_kodati@yahoo.co.in, Abstract- applications, embedded watermark should be invisible, In this paper, we propose a novel hybrid spread robust and have a high capacity. Invisibility refers to spectrum watermarking scheme for authentication of degree of distortion introduced by the watermark and its color images using nonsubsampled contourlet transform affect on the viewers and listeners. Robustness is the and singular value decomposition. The host color image resistance of an embedded watermark against and color watermark images are decomposed into intentional attack and normal signal processing directional sub- bands using Nonsubsampled contourlet operations such as noise, filtering, rotation, scaling, transform and then applied Singular value decomposition cropping and lossey compression etc. Capacity is the to mid frequency sub-band coefficients. The singular amount of data can be represented by embedded values of mid frequency sub-band coefficients of color watermark.[1] watermark image are embedded into singular values of mid frequency sub-band coefficients of host color image in Watermarking techniques may be classified in Red, Green and Blue color spaces simultaneously based on different ways. The classification may be based on the spread spectrum technique. The experimental results type of watermark being used, i.e., the watermark may shows that the proposed hybrid watermarking scheme is robust against common image processing operations such be a visually recognizable logo or sequence of random as, JPEG, JPEG 2000 compression, cropping, Rotation, numbers. A second classification is based on whether histogram equalization, low pass filtering ,median the watermark is applied in the spatial domain or the filtering, sharpening, shearing ,salt & Pepper noise, transform domain. In spatial domain, the simplest Gaussian noise, grayscale conversion etc. It has also been method is based on embedding the watermark in the shown the variation of visual quality of watermarked least significant bits (LSB) of image pixels. However, image for different scaling factors. The comparative spatial domain techniques are not resistant enough to analysis reveals that the proposed watermarking scheme image compression and other image processing out performs the color image watermarking schemes operations. reported recently. Keywords: Color image watermarking, Nonsubsampled Transform domain watermarking schemes such as Contourlet Transform, Singular value decomposition, Peak those based on the discrete cosine transform (DCT), the signal to noise ratio, normalized Correlation coefficient. discrete wavelet transform (DWT), contourlet transforms along with numerical transformations such 1. INTRODUCTION: as Singular value Decomposition (SVD) and Principle In recent years, multimedia products were rapidly component analysis (PCA) typically provide higher distributed over the fast communication systems such image fidelity and are much robust to image as Internet, so there exist strong requirement to protect manipulations.[2]Of the so far proposed algorithms, the ownership and authentication of the multimedia wavelet domain algorithms perform better than other data. Digital watermarking is a method of securing the transform domain algorithms since DWT has a number digital data by embedding additional information called of advantages over other transforms including time water mark into the digital multimedia content. This frequency localization, multi resolution representation, embedding information can be later extracted from or superior HVS modeling, and linear complexity and detected in the multimedia to make an assertion about adaptively and it has been proved that wavelets are the data authenticity. Digital watermarks remain intact good at representing point wise discontinuities in one under transmission/transformation, allowing us to dimensional signal. However, in higher dimensions, protect our ownership rights in digital form. Absence of e.g. image, there exists line or curve-shaped watermark in a previously watermarked image would discontinuities. Since, 2D wavelets are produced by lead to the conclusion that the data content has been tensor products of 1D wavelets; they can only identify modified. A watermarking algorithm consists of horizontal, vertical, diagonal discontinuities (edges) in watermark structure, an embedding algorithm and images, ignoring smoothness along contours and extraction or detection algorithm. In multimedia curves. Curvelet transform was defined to represent two 100 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 dimensional discontinuities more efficiently, with least 2. NONSUBSAMPLED CONTOURLET square error in a fixed term approximation. Curvelet TRANSFORM transform was proposed in continuous domain and its discretisation was a challenge when critical sampling is The Nonsubsampled contourlet transform is a new desired. Contourlet transform was then proposed by DO image decomposition scheme introduced by Arthur and Vetterli as an improvement of Curvelet transform. L.Cunha, Jianping Zhou and Minh N.Do [8]. NSCT is The Contourlet transform is a directional multi more effective in representing smooth contours in resolution expansion which can represents images different directions of in an image than contourlet contains contours efficiently. The CT employs transform and discrete wavelet transform. The NSCT is Laplacian pyramids to achieve multi resolution fully shift invariant, Multi scale and multi direction decomposition and directional filter banks to achieve expansion that has a fast implementation. The NSCT directional decomposition [3] Due to down sampling exhibits a similar sub band decomposition as that of and up sampling, the Contourlet transform is Shift contourlets, but without down samplers and up samplers variant. However shift invariance is desirable in image in it. Because of its redundancy the filter design problem analysis applications such as edge detection, Contour of nonsubsampled contourlet is much less constrained characterization, image enhancement [4] and image than that of contourlet. The NSCT is constructed by watermarking. Here, we present a NonSubsampled combining nonsubsampled pyramids and Contourlet transform (NSCT) [5] which is shift nonsubsampled directional filter bank as shown in invariant version of the contourlet transform. The figure (1).The nonsubsampled pyramid structure results NSCT is built upon iterated nonsubsampled filter banks the multi scale property and nonsubsampled directional to obtain a shift invariant image representation. filter bank results the directional property. In all above transform domain watermarking techniques including NSCT the watermarking bits would be directly embedded in the locations of sub band coefficients. Though here the visual of perception of original image is preserved, the watermarked image when subjected to some intentional attacks like compression the watermark bits will get damaged. Coming to the spatial domain watermarking using numerical transformation like SVD (Gorodetski [6], liu et al [7]) they provide good security against tampering and common manipulations for protecting rightful ownership. But these schemes are non adaptive, thus unable to offer consistent perceptual transparency of watermarking of different images. To provide adaptive transparency, robustness to the compressions and (a) (b) insensitivity to malicious manipulations, we propose a Figure 1 The nonsubsampled contourlet transform (a) novel image hybrid watermarking scheme using NSCT nonsubsampled filter bank structure that implements the NSCT. and SVD. (b) Idealized frequency partitioning obtained with NSCT 2.1 Nonsubsampled Pyramids In this paper, proposed method is compared with another which is based on Contourlet Transform and singular value decomposition (CT-SVD). The peak The nonsubsampled pyramid is a two channel signal to noise ratio (PSNR) between the original image nonsubsampled filter bank as shown in figure and watermarked image and the normalized correlation 2(a).The H0(z) is the low pass filter and one then sets coefficients (NCC) and bit error rate (BER) between H1(z) =1-H0(z). the corresponding synthesis filters the original watermark and extracted were calculated G0(z) =G1(z)=1. with and without attacks. The results show high improvement detection reliability using proposed the perfect reconstruction condition is given by method. The rest of this paper is organized as follows. Bezout identity Section 2 describes the Nonsubsampled contourlet transform, section 3 describes singular value decomposition, section 4 illustrates the details of H0(z)G0(z)+H1(Z) G1 (Z) =1………………(1) proposed method, in section 5 experimental results are discussed without and with attacks, conclusion and future scope are given in section 6. 101 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 : (a) (b) Figure (2): Nonsubsampled pyramidal filter (a). Ideal frequency response of nonsubsampled pyramidal filter (b).The cascading analysis of three stages nonsubsampled pyramid by iteration of two channels Nonsubsampled filter banks . Multi scale decomposition is achieved from invariant is constructed by eliminating the down and nonsubsampled pyramids by iterating the up samplers in the DFB.The ideal frequency response nonsubsampled filter banks by up sampling all filters of nonsubsampled filter banks is shown in figure3 (a) by 2 in both direction the next level decomposition is achieved. The complexity of filtering is constant To obtain multi directional decomposition, the whether the filtering is with H(z) or an up sampled nonsubsampled DFBs are iterated. To obtain the filter H(z m ) computed a Trous algorithm The next level decomposition, all filters are up cascading of three stage analysis part is shown in sampled by a quincunx matrix given by figure 2( b) 1 1 2.2 Nonsubsampled directional Filter Banks: Q= The directional filter bank (DFB) is constructed from 1 ‐1 ……………..(2) the combination of critically-sampled two-channel fan filter banks and resampling operations. The outcome of this DFB is a tree-structured filter bank splitting the 2-D frequency plane into wedges. The The analysis part of iterated nonsubsampled filter nonsubsampled directional filter bank which is shift bank is shown in figure 3 (b) (a) (b) Figure (3) Nonsubsampled directional filter bank (a) idealized frequency response of nonsubsampled directional filter bank.(b) The analysis part of an iterated nonsubsampled directional bank. 3. SINGULAR VALUE DECOMPOSITION data, for data compression and data denoising. If A is any N x N matrix, it is possible to find a Singular value decomposition (SVD) is a decomposition of the form popular technique in linear algebra and it has applications in matrix inversion, obtaining low dimensional representation for high dimensional 3 102 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 A=USVT including discrete cosine transform (DCT), discrete wavelet transform (DWT), Contourlet transform (CT) etc have been used to embed watermark into original image. here the proposed scheme uses nonsubsampled contourlet transform(NSCT) along with SVD for watermarking to obtain better performance compared to existing hybrid algorithms. 4. PROPOSED ALGORITHM Where U and V are orthogonal matrices of order N x N and N x N such that UTU=I,VTV=I , and the In this paper, Nonsubsampled Contourlet diagonal matrix S of order N x N has elements λi Transform and SVD based hybrid technique is (i=1,2,3,..n) , I is an identity matrix of order N x N. proposed for color image watermarking that uses The diagonal entries are called singular values of true color images for both watermark and host matrix A, the columns of U matrix are called the left images. The robustness and visual quality of singular values of A, and the columns of V are watermarked image is tested with three quantifiers called as the right singular values of A. such as PSNR, NCC and Bit Error Rate. It is The general properties of SVD are [1], [2], [9] investigated whether the NSCT-SVD advantages a) Transpose: A and its transpose AT have the over CT-SVD for color image watermarking with same non-zero singular values. their extra features would provide any significance b) Flip: A, row-flipped Arf, and column- in terms of watermark robustness and invisibility. flipped Acf have the same non-zero singular values. 4.1 , 4.2 explain the watermark embedding and c) Rotation: A and Ar (A rotated by an extraction algorithm [10],[11] arbitrary degree) have the same non-zero singular 4.1 Watermark Embedding Algorithm values. d) Scaling: B is a row-scaled version of A by The proposed watermark embedding algorithm repeating every row for L1 times. For each non-zero is shown in Figure 4. The steps of watermark singular value λ of A, B has √L1λ. C is a column- embedding algorithm are as follows. scaled version of A by repeating every column for Step1: Separate the R G B color spaces of both L2 times. For each nonzero singular value λ of A, C host and watermark color images. has √L2λ. If D is row-scaled by L1 times and column-scaled by L2 times, for each non-zero Step2: Apply Nonsubsampled Contourlet singular value λ of A, D has √L1L2λ. Transform to the R color space of both host image e) Translation: A is expanded by adding rows and watermark image to decompose them into sub and columns of black pixels. The resulting matrix bands. Ae has the same Non-zero singular values as A. Step3: Apply SVD to mid frequency sub-band of The important properties of SVD from the view CT of R color space of both host and watermark point of image processing applications are: image. 1. The singular values of an image have very Step4: Modify the singular values of mid good stability i.e. When a small perturbation is frequency sub-band coefficients of R color space of added to an image, their singular values do not host image with the singular values of mid change significantly. frequency sub-band coefficients of R color space of watermark image using spread spectrum technique. 2. Singular value represents intrinsic algebraic image properties. i.e. λI’ = λI + α λW., Due to these properties of SVD, in the last few Where α is scaling factor [9], λI is singular value years several watermarking algorithms have been of R color space of host image, λW is singular value proposed based on this technique. The main idea of of R color space of watermark and λI’ becomes this approach is to find the SVD of a original image singular value of R color space watermarked image. and then modify its singular values to embedded the Step5: Apply inverse SVD on modified singular watermark. Some SVD based algorithms are purely values obtained in step4 to get the mid frequency SVD based in a sense that only SVD domain is used sub-band coefficients of watermarked image. to embed watermark into original image. Recently some hybrid SVD based algorithms have been Step6: Apply inverse Nonsubsampled proposed where different types of transform domain Contourlet Transform to the mid frequency sub- 4 103 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 band coefficients obtained in step 5 to get the R Step5: Apply inverse SVD to obtain mid color space of watermarked image. frequency coefficients of R color space of transformed watermark image using Step 3. Step7: Apply the same Steps from Step2 to Step6 for the G and B color subspaces. Step6: Apply inverse NSCT using the coefficients of the mid frequency sub-band to obtain Step 8: Combine the R,G and B color spaces of the R color space of Watermark image. watermarked image to obtain the color watermarked image. Step7: Repeat the Steps 2 to 6 for G and B color spaces. Step8: Combine the R,G and B color spaces to get the color watermark. Figure 4 Watermark Embeddign Algorithm Figure 5 Watermark Extracting Algorithm 4.2 Watermark Extraction Algorithm The watermark extraction algorithm is shown in Figure 5. The Steps of watermark extraction 5. EXPERIMENTAL RESULTS algorithm are as follows. In the experiments, we use the true color “tajmahal.jpg” of size 256X256 as host image as Step1: Separate the R,G,B color spaces of shown in the Figure 6 and true color “lena.jpg” of watermarked image. size 128 X 128 as watermark as shown in Figure 7. Step2: Apply Nonsubsampled Contourlet The experiment is performed by taking scaling Transform to the R color space obtained in step1. factor alpha as 0.5.The results show that there are no perceptibly visual degradations on the watermarked Step3: Apply SVD to mid frequency sub-band of image shown in Figure 8 with a PSNR of R color space of transformed watermarked image. 45.2253dB. Extracted watermark without attack is shown in Figure 9 with NCC around unity and BER Step4: Extract the singular values from mid of 0.1339. MATLAB 7.6 version is used for testing frequency sub-band of R color space of the robustness of the proposed method. watermarked and host image The proposed algorithm is tested for different host i, e λW = ( λI’ - λI )/ α images such as “lotus.jpg”, ”Baboon.jpg”, Where λI is singular value of watermarked image. ”Barbara.jpg”, ”Way.jpg” ,”Horse.jpg” and “Wheel.jpg” as shown in Table 1 and it is observed that there are no visual degradations on the respected 5 104 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 watermarked images. For all the different Host test color space of host image using eq.3 [12]. The images, the watermark is effectively extracted with final PSNR of watermarked image is taken as mean around unity NCC. Various intentional and non- of PSNR obtained with three color spaces. The intentional attacks are tested for robustness of the similarity of extracted watermark with original proposed watermark algorithm includes watermark embedded is measured using NCC. The JPEG,JPEG2000compressions, low pass filtering, NCC is calculated using eq. (4) [13]for the three Rotation, Histogram Equalization ,Median Filtering, color spaces and their mean is taken as the resultant Salt &Pepper Noise, Weiner Filtering, Gamma Normalized Correlation coefficient. The proposed Correction, Gaussian Noise, Rescaling, Sharpening method is also tested for binary and grayscale Blurring ,Contrast Adjustment ,Automatic cropping, watermark image of size 128x128 and watermarked Dilation, Row Colum Copying, Row Colum and extracted watermark are shown in table 3. removing, color to Gray scale conversion ,shearing and sharpening. The term robustness describes the watermark resistance to these attacks and can be measured by the bit-error rate which, is defined as the ratio of wrong extracted bits to the total number of ……….….(3) embedded bits. Normalized Correlation Coefficient: In table 2, extracted watermark and attacked watermarked image with NCC and BER are shown. The quality and imperceptibility of watermarked image is measured by using PSNR. The PSNR is ………..(4) calculated separately for R, G, B color space of watermarked image with respect to the respective Fig 6:Original image- Fig 7:Watermark image- Fig 8:Watermarked Lena Fig 9:Extracted "Tajmahal.jpg” "Lena.jpg” PSNR= 45.2253 Watermark Ncc=0.9991,Ber=0.1339 TABLE 1: WATERMARKED AND EXTRACTED WATERMARK WITH PSNR, NCC, AND BER FOR DIFFERENT ORIGINAL IMAGES. Original image Watermark image Watermarked image with Extracted image “lotus.jpg” “LENA.jpg” PSNR=46.2785 NCC= 0.9983,Ber=0.1610 6 105 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 Watermarked image with Original image Watermark image Extracted image PSNR=44.8322 “baboon.jpg” “LENA.jpg” NCC=0.9992, Ber=0.1342 Original image Watermark image Watermarked image with Extracted image “barbara.jpg” “LENA.jpg” PSNR=44.4930 NCC=0.9994,Ber=0.1299 Original image Watermark image Watermarked image with Extracted image “way.jpg” “LENA.jpg” PSNR=44.7550 NCC= 0.9994, Ber=0.1140 Original image Watermark image Watermarked image with Extracted image “horse.jpg” “LENA.jpg” PSNR= 44.7308 NCC= 0.9994, Ber=0.1201 Original image Watermark image Watermarked image with Extracted image “wheeljpg” “LENA.jpg” PSNR= 45.5204 NCC= 0.9985, Ber=0.1614 7 106 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 TABLE 2: EXTRACTED WATERMARKS WITH NCC AND BER FOR DIFFERENT ATTACKS ALONG WITH ATTACKED WATERMARKED IMAGE Jpeg compression Ncc= 0.9985,Ber=0.3306 Jpeg2000Ncc= 0.9995,Ber=0.1056 Salt & pepper noise Ncc= 0.6948, Ber=0.4503 Low Pass filtering Ncc= 0.9729 Ber=0.2995 utomatic cropping Ncc= 0.9538 Ber=0.3449 Histogram Equalization Ncc= 0.9808 Ber=0.3128 Rotation Ncc= 0. 0.9951 Ber=0.2958 Median filtering Ncc= 0.9484 Ber=0.3178 Contrast adjustment Ncc= 0.9985 Ber= 0.1613 Weiner filter Ncc= 0.9982 Ber=0.2051 8 107 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 Gamma correction Ncc= 0.9989 Ber=0.1387 Gaussian Noise Ncc= 0.8399 Ber=0.3120 Sharpening Ncc= 0.8379 Ber=0.3967 Gaussian Blurring Ncc= 0.9719 Ber=0.3003 Shearing Ncc= 0.9744 Ber=0.2889 Dilatations= 0.9443 Ber=0.3332 Color to grayscale Ncc= 0.8163 Ber=0.3490 Row & column removal Ncc=0.9977 Ber=0.1930 9 108 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 Row column copying Ncc= 0.9902 Ber=0.9734 Scaling (150%) Ncc = 0.9187 TABLE 3: WATERMARKED AND EXTRACTED WATERMARK IMAGES FOR BINARY AND GRAYSCALE WATERMARK Original image Binary Watermark image Watermarked image Extracted image “tajmahal.jpg “ksp.bmp”. PSNR= 47.6710 Ncc= 0.9995, Ber=0.0157 Original image Binary Watermark image Watermarked image Extracted image “tajmahal.jpg “lena.bmp”. PSNR= Inf Ncc= 1,Ber= 0 Original image “tajmahal.jpg Gray scale Watermark image Watermarked image Extracted image “Lena.jpg”. PSNR=45.2629 Ncc= 0.9992,Ber= 0.1345 salt pepper noise, Rotation, Gaussian Noise, In table 4, the proposed method is compared Sharpening, Row and Colum removal and Row with contourlet and SVD based algorithm [11].It and column copying. demonstrates that proposed method is superior to 10 109 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 TABLE 4: COMPARISON OF CT+SVD AND 7. REFERENCES: NSCT + SVD [1]. C.Venkata Narasimhulu &K.Satya Prasad:”A novel robust watermarking technique based on nonsubsampled contourlet S.No ATTACK Normalized Correlation transform and SVD”, International Journal of multimedia and NSCT+SVD CT+SVD Applications.vol.3, no.1, Feb2011. 1 Jpeg 0.9985 0.9996 compression [2]. C.Venkata Narasimhulu &K.Satya Prasad:”A hybrid 2 Jpeg2000 0.9995 0.9996 watermarking scheme using contourlet transform and 3 Salt & pepper 0.6948 0.6823 singular value Decomposition”, IJCSNS: International Journal of Computer Science and Network Security.vol.10, noise no.9, Sep2010. 4 Low pass 0.9729 0.9839 filtering [3] Minh N. Do, and Martin Vetterli, “The Contourlet 5 Automatic 0.9538 0.9658 Transform: An Efficient Directional Multiresolution Image cropping Representation” IEEE Transaction on image processing, vol 6 Histogram 0.9808 0.9733 14, issue no 12, pp 2091-2106, Dec 2005 Equation [4] Jianping Zhou; Cunha, A.L, M.N.Do, “Nonsubsampled 7 Rotation 0.9958 0.9750 contourlet transform construction and application in 8 Median 0.9484 0.9680 enhancement” IEEE Trans. Image Proc Sept. 2005. filtering 9 Contrast 0.9985 0.9991 [5] Arthur L. Cunha, J. Zhou, and M. N. Do, “Nonsubsampled adjustment contourlet transform: filter design and applications in 10 Weiner filter 0.9982 0.9989 denoising” IEEE International conference on image processing, September 2005. 11 Gamma 0.9989 0.9995 correction [6] V.I.Gorodetski L.J.Popyack, and V.Samoilov, “SVD-based 12 Gaussian Noise 0.8399 0.7538 approach to transparent embedding data into digital 13 Sharpening 0.8379 0.8212 images,” in proc. int. Workshop, MMM-ACNS, 14 Gaussian 0.9719 0.9841 St Peterburg, Russia, May 2001, pp.263-274.10. Blurring [7] R.Liu and T.Tan, “An SVD-Based Watermarking scheme 14 Shearing 0.9744 0.9857 for Protecting rightful ownership,” IEEE Trans. Multimedia, 16 dilatations 0.9443 0.9678 vol.4, no.1, pp.121-128, Mar.2002. 17 Color to 0.8163 0.8693 grayscale [8] A. L. Cunha, J. Zhou, and M. N. Do, “The Nonsubsampled 18 Row & Colum 0.9977 0.9972 contourlet transform: theory, design and applications,” removal IEEE Trans. Image Proc., vol.15, no.10, October 2006. 19 Row Colum 0.9902 0.9820 [9] Emir Ganic and ahmet M. Eskicioglu “ Robust embedding copying of visual watermarks using discrete wavelet transform and 20 Scaling (150%) 0.9187 0.9417 singular value decomposition Journal. Of Electron. Imaging, Vol. 14, 043004 (2005); doi:10.1117/1.2137650 Published 12 December 2005 6. CONCLUSION: [10] Dongyan liu,wenbo Liu,Gong Zhang,”An adaptive In this paper, a novel robust hybrid watermarking watermarking scheme based on nonsubsampled contourlet scheme is proposed for authentication of color transform for color image authentication”.Proceedings of images using nonsubsampled contourlet transform the 2008 the 9th international conference for Young computer Scientist,ISBN:978-0-7695-3398-8. and singular value decomposition. Watermark is embedded in all color spaces of host image by [11] C.Venkata Narasimhulu &K.Satya Prasad:”A new SVD modifying singular values of mid frequency sub band based hybrid color image watermarking for copy right coefficients with respect to watermark mid frequency \ protection using Contourlet transform”, Communicated to International Journal of computer and sub band coefficient with suitable scaling factor. The Applications(IJCA) in March 2011. robustness of watermark is improved for common image procession operations by combining both the [12] Ashraf. K. Helmy and GH.S.El-Taweel “Authentication concepts of nonsubsampled contourlet transform and Scheme Based on Principal Component Analysis for Satellite Images” International Journal of Signal singular value decomposition. The proposed Processing, Image Processing and Pattern Recognition algorithm is tested for different host images and Vol. 2, No.3, September 2009. respective watermark images are obtained without any visual degradation. The proposed algorithm [13] Matlab 7.6 version, Image Processing Tool Box. preserves high perceptual quality of the watermarked image and shows an excellent robustness to attacks like Salt and Pepper Noise, Gaussian Noise, Row Column Copying, and Row Column Removal. 11 110 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011 AUTHORS PROFILE: K.Satya Prasad C.V Narasimhulu Received his Ph.D degree from IIT Madras, India. He He received his Bachelor degree in Electronics and is presently working as professor in ECE department, Communication Engineering from S.V. University, JNTU college of Engineering Kakinada and Rector of Tirupati, India in 1995 and Master of Technology in JNT University, Kakinada, India. He has more than Instruments and Control Systems from Regional 30 years of teaching and research experience. He Engineering College Calicut, India in 2000.He is published 30 research papers in international and 20 currently pursuing the Ph.D degree in the department research papers in National journals. He guided 8 of Electronics and Communication Engineering from Ph.D thesises and 20 Ph.D thesises are under his Jawaharlal Nehru Technological University guidance. His area of interests is digital signal and Kakinada, India. He has more than 15 years image processing, communications, adhoc networks experience of teaching under graduate and post etc.., graduate level. He is interested in the areas of signal processing and multimedia security 12 111 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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