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JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 359 Oblivious Image Watermarking in Discrete Multiwavelet Domain using QIMM N.Leelavathy1, Research Scholar, J.N.T.University, Kakinada, India Email: nleelavathy@gmail.com1 E. V. Prasad2, Professor, C.S.E. Department, J.N.T.University, Kakinada, India S.Srinivas Kumar3, Professor, E.C.E. Department, J.N.T.University, Kakinada, India B. Chandra Mohan4, Professor, E.C.E. Department, Bapatla Engineering College, India Email: {drevprasad@yahoo.co.in2, samay_ssk2@yahoo.com3, chandrabhuma@yahoo.co.in4} Abstract— This paper proposes a novel oblivious image transform (DCT) [5-8], Hadamard Transform (HT), watermarking technique for the copyright protection and Discrete Wavelet Transform (DWT) [9-11], and authentication of still images, based on Discrete Contourlet Transform (CT) [12-14]. Multiwavelet Transform (DMWT) and Quantization Index DWT has a good property of localization in time and Modulus Modulation (QIMM). The multiwavelet frequency. Hence, the wavelets are useful tool in digital coefficients of an image are quantized using QIMM and the coefficients are modified according to the binary image processing and its applications. watermark logo. Watermark extraction does not require Quantization Index Modulation (QIM) data the original image. Experimental results show that, the embedding methods [2-4] are proved to be attractive in proposed watermarking scheme is robust to image a practical and theoretical engineering perspective. In compression and rotational attacks. It is superior to the QIM schemes, the amplitude of a vector whose entries method proposed by Lin et al. [10] in terms of Peak Signal are pixels or frequency coefficients are quantized using to Noise Ratio (PSNR) and Bit Error Rate (BER). a quantization lattice. While this approach provides a gain in the watermark capacity over other content- dependent schemes, QIM based digital watermarking Index Terms— Oblivious image watermarking, Copyright offers less robustness compared to QIMM. protection, Authentication, Discrete multiwavelet, Quantization Index Modulus Modulation. There are various methods which divide the DWT coefficients into blocks. These methods use significant I. INTRODUCTION coefficient from each block to embed watermark bit. The position of the significant coefficient is important in The reproduction and distribution of unauthorized the extraction process of oblivious watermarking. copies of copyright information is the order of the day Lin et al. [10] method maintained the maximum in view of the development of Internet facilities. As a significant coefficient to remain largest in the block result, copyright protection and authentication has even after embedding. Hence, the position of the attracted many scientific and business communities in maximum coefficient is identified in the watermark national and international level. The applications of extraction process. However, the embedding capacity is watermarking are in secret communication, medical very low as only one significant coefficient per block is imaging, broadcast monitoring, content protection, used. If the block size is reduced to increase the number tamper proofing, and fingerprinting. of blocks, PSNR of the watermarked image reduces. The watermarking techniques can be divided into two The scalar wavelets are generated by one scaling categories: spatial domain and transform domain function where as multiwavelets have multiple scaling techniques. The spatial domain techniques are functions. A novel oblivious digital image conceptually simple and have low computational watermarking algorithm in DMWT domain using complexities. However, the spatial domain QIMM is proposed which provides higher embedding watermarking techniques are generally not robust to capacity and robustness to various attacks. intentional or unintentional attacks. The transform This paper is organized as follows: Introduction to domain techniques are generally considered to be robust DMWT is given in Section II. QIMM is introduced in against attacks. The transform domain techniques Section III. The embedding and extraction algorithms embed the watermark by modulating the magnitude of are proposed in Section IV. The experimental results the coefficients in the transform domain such as and analysis are drawn in Section V. Finally, the Discrete Fourier Transform (DFT) [1], Discrete Cosine conclusions are given in Section VI. © 2011 ACADEMY PUBLISHER doi:10.4304/jmm.6.4.359-368 360 JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 II. DISCRETE MULTIWAVELET TRANSFORM This adds several degrees of freedom in multiwavelet design and makes it possible to have several useful In the research field of image processing applications, properties such as symmetry, orthogonality, short wavelet transform has given many advantages over support, and a higher number of vanishing moments traditional DFT, DCT, Walsh, and HT methods in terms simultaneously. The scaling function vector is of reducing the blocking artifacts. In this regard, the T design of filters for various transforms require many (1) desirable properties such as, compact support, satisfies a matrix dilation equation orthogonality, symmetry, and vanishing moments. However, the design of filters for scalar wavelets [15] is . (2) limited. The coefficients are matrices instead of Multiwavelets can achieve better level of scalars. Associated with these scaling functions are performance and higher degree of freedom than scalar wavelets , satisfying the matrix wavelets with approximately similar computational wavelet equation complexity. In the design of multiwavelet filters the desirable properties [16, 17] are achieved (3) simultaneously. T where, (4) In recent years, a very few papers have been reported using DMWT in watermarking applications. Some of is a vector and the are matrices. the papers have reported moderate robustness than When i.e., one scaling function and one compared to scalar wavelets. An invisible watermark or wavelet function, the multiwavelet system reduces to logo is fused into the host image [18, 19]. Kwon and the scalar wavelet system. Tewfik [33] proposed adaptive digital watermarking In practice, multiscaling and wavelet functions are that uses successive subband quantization and concerned with multiplicity . The scaling perceptual modelling using DGHM multiwavelet. The functions and are orthogonal multiscaling watermark is Gaussian random sequence and it needs functions and is a matrices. Similarly, the original watermark sequence for detection. and are two wavelet filters and is a Kim et al. [34] proposed adaptive watermarking scheme matrices. An important example is constructed by using the properties of the edge and texture region of the GHM (Geronimo, Hardin and Massopust) system [25]. original image. Image dependent parameters are Some salient features to choose multiwavelets are assumed which determines the robustness of the summarized below : algorithm. Unlike most of the perceptual models, The extra degrees of freedom that is inherent in Ghouti et al. [31, 32] proposed balanced multiwavelet multiwavelets has reduced the restrictions on the filter using JND profile of the image. Serdean et al. [28] properties. Further, it is well known that a scalar proposed much simpler perceptual model which uses wavelet cannot simultaneously have both orthogonality blind spread-spectrum approach for watermark and symmetric property [15]. Symmetric filters are embedding. The data hiding capacity has improved over necessary for symmetric signal extension, while previous methods. Efforts have been made to take orthogonality makes the transform easier to design and advantage of machine learning techniques for implement. The short support and vanishing moments watermark embedding and extraction [21-23]. These are generally preferred to achieve a better localized algorithms depend on the ability of learning machine approximation of the input function. Multiwavelets are and its learning algorithm. Many algorithms used able to possess the best of all these properties optimization techniques [35-38] to improve robustness simultaneously and is not possible in scalar wavelets of the watermark by dynamically adjusting digital [17]. watermark embedding positions. Prayoth et al. [39] Multiwavelets have good energy compaction proposed embedding technique based on construction of properties which can decorrelate the signal into a multiwavelet tree to embed watermark. Day et al. [30] smaller number of scaling coefficients containing most has integrated QIMM into multiple description of the energy. These coefficients can be used for watermarking technique. Quantization methods have watermarking which are better resistant to image proven good in wavelet [20] and CT [12] domain. The compression, rotation and other attacks. maximum wavelet coefficients are quantized and Hence, multiwavelets can perform better than scalar watermarked for copyright protection. These algorithms wavelets with similar computational complexity in are generally robust for geometrical attacks [10]. image processing. Multiwavelets are introduced as a more powerful multiscale analysis tool. Multiwavelet is defined using III. QUANTIZATION INDEX MODULUS MODULATION several wavelets with several scaling functions [16, 25]. When a Multi-Resolution Analysis (MRA) is Quantization is a process of approximating the generated using multiple scaling functions and wavelet continuous set of values in the image data with functions, it gives rise to the notion of multiwavelets preferably small finite discrete set of values. The [24-28]. A multiwavelet with ‗r‘ scaling functions and quantizer input is the original data, and the output is ‗r‘ wavelet functions is said to have multiplicity ‗r‘. always one level among a finite number of levels. © 2011 ACADEMY PUBLISHER JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 361 There are three common scalar quantization methods: where, 1. Dither Modulation (DM) 2. Quantization Index Modulation (QIM) Δ 3Δ 8 v1 < 3. Quantization Index Modulus Modulation 8 (QIMM). 5Δ 3Δ 7Δ In the previously proposed watermarking algorithms, z' = ≤ v1 < (7) wavelets are efficiently used with the DM [20], 8 8 8 QIM [3, 4] and QIMM [12, 30, 40]. QIMM 9Δ 7Δ implementation is simpler than the dither quantization. 8 ≤ v1 < Δ In any watermarking algorithm there is always a 8 trade-off between robustness against attacks and quality of the image. Quality is determined by PSNR of the case (ii): If wi then, x' = xi - v1 + z'' i (8) watermarked image. Generally, higher degree of robustness is achieved by higher quantization step size. where, But, higher quantization step size reduces the PSNR of the watermarked image. The embedding quantization Δ Δ step size δQIM of QIM is almost equal to two times of - 8 v1 < 8 δQIMM of QIMM, i.e., QIMM can achieve same mean 3Δ Δ 5Δ square error with half of the quantization step size in z'' = ≤ v1 < (9) QIM [30]. Therefore, a better robustness and PSNR is 8 8 8 obtained in QIMM than compared to QIM keeping 7Δ 5Δ quantization step size constant. The significant 8 ≤ v1 < Δ coefficients obtained by wavelet transform are of the 8 order of 100s. Hence, the quantization steps used in At the detector, the watermarked signal is wavelet transform are from 5 to few 10s [30]. The significant coefficients of Multiwavelet transform are of X' = {x1',x2',...,xn'} the order 1000s. Therefore, the quantization steps can be higher values, with good PSNR and higher robustness and is detected as follows: against attacks. There are two methods in QIMM. Δ Δ 3Δ 1) Method 1: The host signal (coefficients obtained 0 0 ≤ v2 < or ≤ v2 < after multiwavelet transform) is divided by the 4 2 4 quantization step size ( ) and the nearest integer value w' = (10) i 1 Δ Δ 3Δ is obtained. This value is then executed with modulo 2 ≤ v2 < or ≤ v2 < Δ to obtain the remainder as 0 or 1. Then, if remainder is 4 2 4 equal to the watermark bit value, then the reconstructed value is the quantized host signal, else it is biased either where, v2 = mod(xi', ) (11) +1 or -1 [30]. 2) Method 2: Modulo quantization step ( ) is IV. PROPOSED WATERMARKING SCHEME executed to the host signal and the remainder is A. Prefiltering of Host Image subtracted from the host signal. The error signal is added to the result which is near to the host signal Multiwavelets differ from scalar wavelet systems as it depending on the watermark bit 1 or 0 [12]. A needs several input streams rather than one. This maximum error signal of {+ /4 or - /4} is added so necessitates a pre-filtering operation on the input the noise immunity is /4. stream. This pre-filtering operation is also called multi- The method 2 is used in the paper as the coefficients wavelet initialization and can be performed in a obtained using multiwavelet transform are very large in critically sampled or an over-sampled fashion. size. QIMM used in this proposed algorithm is Prefiltering based on Strela‘s algorithm [16] is used. In described as follows: this algorithm, over-sampled procedure is used where In the embedding process, if the host signal is two identical rows are taken as input to the multifilter bank. This procedure is also called Repeated Rows X = {x1 ,x2 ,....,xn } (RR). It introduces over-sampling of data by a factor of 2. RR prefiltering have proven useful in many of the and the watermark logo bits, wi Let be the image processing applications like feature extraction, denoising, etc. As the data redundancy can increase the Quantization step. To modify the host signal X to X' , embedding capacity, the RR prefiltering is employed in the following cases are considered. this paper. Suppose v1 = mod(xi , ) (5) B. The Watermark Embedding Procedure Due to the versatility and accuracy of the GHM, it is case (i): If wi then, x' = xi - v1 + z' i (6) used in this paper. Experimentation has been performed © 2011 ACADEMY PUBLISHER 362 JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 in different levels. The multichannel nature of The flow chart for watermark embedding algorithm is multiwavelets yields a different sub-band structure shown in Fig. 3(a). compared with scalar wavelets. Multiwavelet sub-bands of 2-dimensional image using first level decomposition Host Image N × N produce 16 sub-bands as shown in Fig. 1. L1L1 L2L1 H1L1 H2L1 Perform RR Prefiltering Secret Key K L1L2 L2L2 H1L2 H2L2 2N × 2N Compute GHM (kth level) Watermark Logo p × m L1H1 L2H1 H1H1 H2H1 Multiwavelet Transform L1H2 L2H2 H1H2 H2H2 Figure 1. First Level multiwavelet decomposition Select Low-Low Pass sub Scrambled Watermark Logo band p×m a b Modify the selected coefficients xi using QIMM as per watermark logo bits c 1 0 Watermark Bit d Figure 2. Three level DGHM multiwavelet decomposition Generate z‘ using (9) and Generate z‘ using (7) and a)Prefiltering b) 1st Level decomposition c) 2nd Level Modify xi as in (8) Modify xi as in (6) decomposition d) 3rd Level decomposition The four ‗low-low-pass‘ sub-bands are further Compute Inverse GHM decomposed to sub-bands in the next level of Multiwavelet decomposition. This process continues number of times for kth level of decomposition. The number of sub matrices will be equal to where k is the number RR Postfiltering of levels of decomposition. This can be observed in Fig. 2 for 3rd level GHM multiwavelet decomposition. Watermarked Image The steps of watermark embedding algorithm are N×N as follows: Figure 3(a). Flow chart for watermark embedding algorithm 1. The host image of size N × N is prefiltered using RR prefilter to produce two input C. The Watermark Extraction Procedure streams. The size of the image after RR prefiltering is 2N × 2N The steps of watermark extraction algorithm are as follows: 2. GHM multiwavelet transform is applied to the host image after prefiltering. A level decomposition is performed on the image. The 1. The watermarked image of size N × N , is ‗low-low-pass‘ sub-bands of level are prefiltered using RR prefilter. The size of the selected for watermark embedding where it image after RR prefiltering is 2N × 2N ,. concentrates its most of the energy. 2. The GHM multiwavelet transform is applied to 3. To increase the watermark security, the the resultant image after prefiltering. A kth watermark logo is scrambled with a secret key. level decomposition is performed on the image. It is impossible to obtain the original 3. The bits of wi ' will be extracted from the watermark logo without the secret key. multiwavelet coefficients of selected blocks 4. The bits of wi will be embedded into the using QIMM method as in (10) using (11) multiwavelet coefficients of selected blocks described in section III. using QIMM method proposed in section III. 4. The bits obtained are descrambled with the 5. The inverse GHM multiwavelet is applied to secret key. the modified sub-bands. 5. After extracting the watermark, BER is used to 6. The RR post-filtering is performed and the quantify the correlation between the original final watermarked image is obtained. watermark and the extracted one. © 2011 ACADEMY PUBLISHER JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 363 The flow chart for watermark extraction algorithm is shown in Fig. 3(b). Watermarked image N × N Perform RR Prefiltering 2N×2N (i) (ii) Figure 4(a). (i) Original Baboon image, (ii) Watermarked Baboon Compute GHM( kth level) image without attack Multiwavelet Transform Select Low-Low Pass sub band Extract w ' using QIMM i (i) (ii) (iii) as in (10) using (11) Secret Key K Descrambled Watermark Logo p × m (iv) (v) (vi) Extracted watermark Logo p × m Figure 4 (b). (i)-(iii) Original watermark logo of sizes , and respectively & (iv)-(vi) Scrambled watermark Compute BER between logo of sizes , and respectively original and extracted Let the host signal be X and the watermarked signal watermark be X' and are of size N × N , then PSNR in dB is given by Figure 3(b). Flow chart for watermark extraction algorithm V. EXPERIMENTAL RESULTS AND ANALYSIS N 2 max(max(X))2 PSNR = 10log10 N N (11) To evaluate the performance of the proposed (X - X') 2 watermarking scheme, experiments have been conducted in which a GHM multiwavelet is used to x=1 y=1 decompose the original image using RR pre-processing. where, X and X' indicate the pixel intensities of host The number of transform coefficients are more in RR and watermarked signals respectively. pre-processing than compared to ‗critically sampled‘ Various multiwavelets like GHM, c1, Sa4, Bih52s, pre-processing, i.e., for an image of , the Bih54n [15], and a scalar Haar wavelet are used. Haar transform coefficients of RR are whereas wavelet gave very inferior PSNR value as the is very in ‗critically sampled‘ pre-processing it is high. Haar wavelet coefficients are of the order of only. Hence, embedding capacity is more in RR than hundreds, where as multiwavelet coefficients are of the compared ‗critically sampled‘ pre-processing. order of thousands in ‗low-low pass‘ band. Almost all The original image is a pixels gray multiwavelets gave similar PSNR values but GHM scale image. Images like Baboon, Lena, Boats and outperformed as shown in Figs. 5 & 6. The Peppers are taken from Stirmark image database of experimental results also show that, RR pre-processing Fabien Petitcolas. The original Baboon image and method is giving better PSNR than the ‗critically watermarked Baboon image are shown in Fig. 4(a) (i) - sampled‘ method for various multiwavelets. But, (ii). The binary watermark logo of , computational complexity of RR is higher than the and are chosen in the paper are as shown in ‗critically sampled‘ pre- and post-processing. Fig. 4(b) (i)-(iii) and Fig. 4(b) (iv)-(vi) shows the For a given size of watermark logo, as the corresponding scrambled watermark logo. number of multiwavelet decomposition levels increased, BER is a very useful measure of the performance. In computational complexity increases and also, the PSNR this case, the bit error rate is calculated as the number of of the image decreases. It means that, the embedding incorrectly decoded bits divided by the total number of capacity of the watermarked image has decreased for a embedded bits in the watermarked image. given robustness towards attacks. © 2011 ACADEMY PUBLISHER 364 JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 Values of psnr in dB for various Multiwavelets TABLE I. (a) 60 Comparison of the proposed method with Lin et al. method Comparison Lin et al. [10] Proposed method 50 Wavelet Multiwavelet Transform Domain 40 (Haar ) ( GHM) PSNR in dB 30 Decomposition Level Level : 3 Level : 3 20 PSNR in dB 10 (logo: ) 40.3172 48.8454 0 bih52 bih54 haar ghm cl sa4 s n Max. Embedding . ∆=160 6.7711 50.678 47.497 47.489 49.604 49.815 Capacity ∆=180 6.8018 49.666 46.207 45.847 48.585 48.794 Figure 5. PSNR of various multiwavelets at ∆=160 and for cover image baboon using ‗repeated rows‘ pre-processing and post- TABLE I. (b) processing.with a logo size of . Comparison of the proposed method with Lin et al. method with attacks Values of PSNR in dB for various Multiwavelets Attacks / BER Lin et al. Proposed 60 [10] method Low Pass Filtering ( ) 0.0742 0.0625 50 Median Filtering ( ) 0.1035 0.0859 40 Resizing ( 512-256-512) 0.0234 0.0195 PSNR in dB Rotation ( 1 Degree) 0.0352 0.0332 30 JPEG ( QF=50) 0.0117 0 JPEG ( QF=60) 0.0078 0 20 Cropping ( ¼) 0.2383 0 Bit Plane Removal : 0.0039 0 10 (2nd LSB) 0 ghma bih5a bih54 haar p clap sa4ap p n 65 ∆=160 6.7711 50.166 29.184 47.184 31.237 31.093 55 PSNR(in dB) ∆=180 6.8018 48.883 29.399 45.878 31.106 30.848 45 Figure 6. PSNR of various multiwavelets at ∆=160 and ∆=180 for a 35 cover image Baboon using ‗critically sampled‘ pre-processing and 200 15 21 25 40 60 80 100 120 140 160 180 post-processing with a logo size of . Multiwavelet : GHM Quantization Step 42.2 Figure 8. PSNR in dB vs Quantization 42 41.8 0.55 0.5 PSNR in dB 41.6 0.45 41.4 0.4 0.35 41.2 BER 0.3 0.25 JPEG2000 41 0.2 JPEG 40.8 0.15 0.1 40.6 0.05 Baboon Peppers Lena Boats 0 PSNR 41.2328 41.3366 41.3731 42.1028 10 20 30 40 50 60 75 90 100 Quality Factor (QF) Figure 7. PSNR of various images for ‗GHM‘ multiwavelet at ∆=170 using ‗repeated rows‘ pre-processing and post-processing with a logo Figure 9. BER vs JPEG and JPEG2000 compression of recovered size of logo size of © 2011 ACADEMY PUBLISHER JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 365 TABLE II. BER after attacked by JPEG comparison with various quality factors (QF) for Baboon image for different sized logos BER/ QF 10 20 30 40 70 100 0.1074 0.1270 0 0 0 (a) Logo : 0.4883 NA (< 0.2) 0.1863 0.0398 0.0046 0 0 (b) Logo : 0.4844 NA (< 0.2) TABLE III. BER after Rotational attacks for Baboon image for different sized logos BER/ Rotation (Degree) 0.25ᵒ 0.3ᵒ 1ᵒ 10ᵒ 50ᵒ 0.0303 0.0273 0.0195 0.0732 0.1592 (a) Logo : 0.0273 0.0271 0.0344 0.0415 0.0732 (b) Logo : TABLE IV. BER for various attacks BER/ Other attacks Salt & Pepper Noise Row Column Blanking Row Column Copying Cropping ( ¼) 0 0.0117 0.0850 0.0381 (a) Logo : 0.0132 0.0845 0.0398 0 (b) Logo : BER/ Other attacks Bit Plane Removal Gaussian Noise Low Pass Filtering Resizing ( 512-256-512) ( 2nd LSB) 0 0.0361 0.0654 0.0225 (a) Logo : 0 0.0752 0.1287 0.0764 (b) Logo : © 2011 ACADEMY PUBLISHER 366 JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 The quantization step level chosen is and the cropping, and bit plane removal. The proposed cover image is baboon. The PSNR is calculated taking algorithm can be applied to image copyright protection different multiwavelet decomposition levels, i.e., from and authentication. level 1 to level 8. Taking into consideration of embedding capacity and robustness against attacks, REFERENCES multiwavelet decomposition level 3 is found to be [1] O‘ Ruanaidh. J.J.K., Dowling W.J., and Boland F.M., appropriate. 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Srikaew, ―An optimal robust digital image watermarking [19] Kurugollu F., Bouridane A., Roula M., and Boussakta based on Genetic algorithms in multiwavelet Domain,‖ S.: ―Comparison of different wavelet transforms for WSEAS Transactions on Signal Processing, issue 1, fusion based watermarking applications,‖ Proc.10th IEEE volume 5, Jan. 2009. Int. Conf. on Electronics, Circuits and Systems, 14–17 [37] Jian Luo, Yinghui Pan, and Liang Tan, ―Digital December 2003, vol. 3, pp. 1188–1191. watermarking multi-objective optimization based on [20] Xinshan Zhu, and Zhi Tang, ―Improved Quantization multi-wavelet,‖ IEEE International conference on control Index Modulation watermarking robust against amplitude and automation, Dec., 2009. scaling distortions, IEEE International conference on [38] Pan Yinghui, ‖Digital watermarking particle swarm Multimedia and Expo, 2008. optimization based on multi-wavelet‖, Journal of [21] Zhang, J., Wang, N.-C., Xiong, F., ―Hiding a logo Convergence Information Technology, Vol. 5 , No. 3, watermark into the multiwavelet domain using neural 2010. networks,‖ In: Proceedings of 14th IEEE International [39] Prayoth K, Attakitmongcol K and Srikaew A, ―A robust Conference on Tools with Artificial Intelligence, ICTAI - image watermarking scheme using multiwavelet tree,‖ 2002. Proceedings of the World Congress on Engineering, [22] Li C.-H., Lu Z.-D., and Zhou K., ―An image vol. I, July, 2007. watermarking techniques based on support vector [40] Pierre Moulin, and Anil Kumar G, ―Block QIMM regression,‖ Proceedings of ISCIT, pp. 177–180, 2005. Watermarking games,‖ IEEE Transaction on Information [23] Hong Peng, Jun Wang C, and Weixing Wang, "Image Forensics and Security, Vol 1, Issue 3, Sep., 2006. watermarking method in multiwavelet domain based on support vector machine,‖ The Journal of systems and Software, pp. 1470-1477, 2010. N. Leelavathy, is currently working as [24] Shen L.X, Tan H.H, and Tham J.Y, ―Symmetric- Professor and HOD, Computer Science antisymmetric orthogonal multiwavelets and related and Engineering Department, Krishna‘s scalar wavelets,‖ Appl. Comput. Harmonic Anal. 8 (3), Pragati Institute of Technology, pp. 258–279, 2000. Rajahmundry, India. She is working [25] Geronimo J., Hardin D., and Massopust P., ―Fractal towards her Ph.D. at Jawaharlal Nehru Functions and Wavelet Expansions Based on Several Technological University, Kakinada, Functions,‖ Journal of Approximation Theory, vol. 78, India. She received her M.Tech. in pp. 373-401, 1994. Computer Science and Engineering from [26] Tham J., Shen L., Lee S., and Tan H., ―A General Jawaharlal Nehru Technological University, Hyderabad, India Approach for Analysis and Application of Discrete in 2003. She did her B.E. in Electronics & Communication Multiwavelet Transforms,‖ IEEE Transactions Signal Engineering from Vasavi College of Engineering, Hyderabad, Processing, vol. 48, pp. 457- 464, 2000. India in 1992. She has eleven years experience of teaching [27] Md. Khalil I, ― Digital image watermarking : Scalar undergraduate students and post graduate students. Her Wavelet versus Multiwavelets,‖ IEEE Trans., 2006. research interests are in the areas of Digital Image Processing, [28] Serdean C.V, Ibrahim M.K, Moemeni A, and Al-Akaidi Image Watermarking, and Cryptography and Network M.M, ―Wavelet and Multiwavelet watermarking,‖ The Security. Institution of Engineering and Technology, 2007. [29] Soman K. and Ramachandran K., Insight into Wavelets E. V. Prasad, is working as Director, from Theory to Practice, Prentice Hall of India: New Institute of Science and Technology, Delhi, 2004. Jawaharlal Nehru Technological [30] Miin- Luen Day, Suh-Yin Lee and I-Chang Journal, University, Kakinada, India. He received ―Multiple Description Watermarking Based on his Ph.D. in Computer Science & Quantization Index Modulus Modulation,‖ Journal of Engineering from University of Roorkee Information Science and Engineering, 1785-1800, 2007. in 1990. He did his M.E. in Control [31] Ghouti L, Bouridane A, and Boussakta S, ―High capacity Systems from Madras University in 1978. watermarking using balanced multiwavelet transforms,‖ He received his B.E. in Electronics & IEEE, 2005. Communication Engineering from S V University in 1975. He [32] Ghouti L, Bouridane A, Md. Ibrahim K, and Boussakta S, has thirty three years of experience in teaching undergraduate ―Digital image watermarking using balanced students and post graduate students. He has taught over 15 multiwavelets,‖ IEEE transactions on signal processing, courses in CSE and supervised 4 Ph.D students. He held Vol. 54, No. 4, April, 2006. different positions in his carrier like Head of the Department, [33] Kwon Ki R and Tewfik A H, ―Adaptive watermarking Vice Principal, Principal and BOS Chairman. He is the Co using successive subband quantization and perceptual author of four books and published more than twenty papers in model based on multiwavelet transform,‖ Proc. SPIE- National and International journals and 53 in various © 2011 ACADEMY PUBLISHER 368 JOURNAL OF MULTIMEDIA, VOL. 6, NO. 4, AUGUST 2011 Conferences. His research interests include Parallel Computing, Data Mining, and Information Security. S. Srinivas Kumar is currently working as Professor and Director, Sponsored Research, Jawaharlal Nehru Technological University, Kakinada, India. He received his Ph.D. from E&ECE Department IIT, Kharagpur. He received his M.Tech. from Jawaharlal Nehru Technological University, Hyderabad, India. He has twenty one years experience of teaching undergraduate and post-graduate students and guided number of post-graduate thesis. He has published 30 research papers in National and International journals. Presently he is guiding ten Ph.D. students in the area of Image processing. His research interests are in the areas of digital image processing, computer vision, and application of artificial neural networks and fuzzy logic to engineering problems. Chandra Mohan B is currently working as Professor & Dean, academics, Bapatla Engineering College, Bapatla, India. He received his Ph.D. from JNTU College of Engineering, Kakinada, India. He received his M.Tech. from Cochin University of Science & Technology, Cochin, India. He has seventeen years experience of teaching undergraduate students and post graduate students. He has published seven research papers in National and International journals. His research interests are in the areas of image watermarking, and image compression. © 2011 ACADEMY PUBLISHER

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