A Robust and Oblivious Watermarking Method Using Wavelet Transform and Genetic Algorithm

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1 .iecs.in International Journal Of Engineering And Computer Science ISSN:39-74 Volume 5 Issue -0 February, 06 Page No A Robust and Oblivious Watermarking Meod Using Wavelet Transform and Genetic Algorim Dr. K. Ramananeyulu, K.S.S. Manasa, G. Upendra Professor, ECE, PVP Siddhara Institute of Technology, AP, India -id: kongara.raman@gmail.com Dept. of ECE, PVP Siddhara Institute of Technology, AP, India -id: manasa.koduganti@gmail.com Dept. of ECE, PVP Siddhara Institute of Technology, AP, India -id: upendra.pvpsit@gmail.com ABSTRACT Watermark is embedded by modifying e ird level mid frequency coefficients of e host image i multiple SFs. As many combinations of SFs are possible, it is difficult to obtain optimal solutions by trial and error meod. Hence, in order to achieve e highest possible transparency and robustness, optimization of e scaling factors is necessary. This task employs Genetic Algorim (GA) to obtain optimum SFs. GA can search for multiple solutions simultaneously over a ide range, and an optimum solution can be gained by combining e obtained results appropriately. The aim of e task is to develop an optimal atermarking technique based on DWT domain for grey-scale images. In is paper, a robust and oblivious image atermarking algorim using imum avelet coefficient modulation is proposed. Simulation results sho at performance of e proposed meod is superior in terms of Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (). Index Terms: Digital Image Watermarking, Wavelet Transform, Genetic Algorim, Copyright Protection, Peak Signal to Noise Ratio, Normalized Correlation Coefficient. INTRODUCTION Digital media has become e leading choice of format for storing audio, image, video and text information for multimedia applications on e internet. Media formats such as MP3, DVDs and s are idely available to millions of users. Many researchers are aare of e issues of copyright protection, image auentication, proof of onership etc. Many solutions have been proposed to tackle ese issues. Watermarking Technique is one of ese solutions. In atermarking process, specific information called atermark is embedded imperceptibly into e original media obect. Watermarking algorim is referred to as an oblivious (also referred to as blind or public) if e original image and e atermark are not needed during extraction. Three important issues are to be considered in is system. ) Embedding process should not degrade e quality of e cover image and should be perceptually invisible to maintain its protective secrecy. ) The atermark must be robust enough to resist common image processing attacks and not be easily removable and only e oner of e cover to be able to extract e atermark. 3) The blindness is necessary in some applications in hich e original image (and e atermark also) is not available at e time of extraction. In is paper, a robust and an oblivious image atermarking scheme based on e imum avelet coefficient modulation is proposed. Rest of e paper is organized as: Section provides revie of related ork. Section 3 covers e proposed meod. In section 4, experimental results ill be presented for e improved performance of e proposed meod in comparison i e existing techniques. Robustness against e most common attacks is also presented. Finally, e section 5 discusses conclusion part. Revie of e related orks Watermarking can be done eier in e spatial domain or in e transform domain. Watermarking in e spatial domain is simple but e atermarked image is hard to robust. Watermarking in e transform domain is secure and robust to Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 58

2 many attacks. Plenty of research of various researchers has been done on Image atermarking algorims using Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT). Among ese, e DWT approach remains one of e most effective and easy to implement techniques for image atermarking []. The most important issue in DWT based image atermarking is ho to choose e coefficients to be embedded. In [][3][4], e atermark is embedded in e significant avelet coefficients. Wang et al. [5] proposed a atermarking meod hich is based on multi-reshold avelet coding (MTWC) and e successive sub-band quantization (SSQ) is used to search for e significant coefficients. The atermark is added by quantizing e significant coefficient in e significant band using different eights. In [6], to meods ere proposed: one connects to atermark is embedded by modifying e triplets of significant coefficients according to a sequence of information bits and second one considers e rectangular blocks of coefficients, and each block is used to embed one atermark bit. In [][7][8], e significant coefficients, selected from global coefficients are used and shoed robustness to many image attacks. But, e problem is at e order of extracting e significant coefficients in e extraction process should be exactly e same as ose in e embedding process. Hence, ey are not suitable for e blind atermarking. W.H. Lin et al. [9] used DWT for atermarking a 5 x 5 gray scale image. They quantized e imum avelet coefficient of a variable sized block of a selected sub band. The atermark is a 3 x 6 binary image. Lo embedding capacity and adustment of e scheme parameters to satisfy some specified atermarking requirements (PSNR and i attacks) are e limitations of eir meod. From all ese observations, is paper presents a blind and robust atermarking algorim to embed a 3 x 3 binary atermark into a grayscale cover image using e local imum avelet coefficient modulation, hich is a block based approach. Third level LH sub band is selected for embedding e atermark. Selected sub band is divided into various blocks of equal size. Number of blocks must be equal to e number of atermark bits. Maximum coefficient of a block is modulated depending on heer e atermark bit is or 0. Energy of e imum coefficient is increased if e atermark bit is and makes e energy of e imum coefficient close to e second imum if e atermark bit is 0. During e extraction process, energy is subtracted from e imum coefficient of a block. If e coefficient is still e imum of e block en e decision is taken in favor of a, else in favor of a 0. The scheme is characterized i parameters to get control over e embedding and extraction process. Then, Genetic Algorim (GA) [3-9] is used for parameter optimization. Optimization is required to satisfy e conflicting requirements of e Peak Signal to Noise Ratio (PSNR) and e Normalized Cross correlation (). Experimental results shos at e proposed meod is better an e existing meods [9][0][][] in terms of bo PSNR and. 3 Proposed Watermarking Scheme In is section, proposed scheme is described in ree sub sections. Section 3. deals i atermark embedding DOI: /iecs/v5i.7 procedure, atermark extraction is explained in section 3. and e application of GA for determining e optimum parameters of e scheme is given in section Watermark Embedding In e proposed algorim, a binary atermark image is embedded in a gray scale cover image. The transform used is DWT. The embedding strategy is based on e local imum avelet coefficient modulation. Third level mid-frequency sub-band, LH 3, of e cover image is selected for embedding binary atermark of size N. Divide e LH 3 sub band into N number of blocks. No, compute e folloing. Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 58 (a) M N MWC = N M (), if e atermark bit is = t, oerise () = imum avelet coefficient of e t =scaling factor block (b) a =t imum { avg, MWC x t 3 } for all to N (3) avg =average coefficient value of e t, t 3 Modulate for all are e scaling parameters ne = block according to e Watermark bit to N a as follos, if e atermark bit is sec a oerise (4) of e sec denotes e second imum coefficient value block t 3 is e scaling factor hich is less an. Get e modified LH 3 sub band by combining e modulated blocks. Obtain e ree level inverse DWT using modified LH 3 sub band to get e atermarked image. The parameters / scaling factors; t, t and t 3 ; are used to control e value of e PSNR. 3. Watermark extraction Possibly attacked atermarked image is e only input image required for e extraction process as e scheme is oblivious atermarking meod. Parameter t 4 value is required. Even if

3 DOI: /iecs/v5i.7 e value of t 4 is not available, GA may be used to find its value. Extraction of atermark is done as follos. Decompose e possibly attacked atermarked image using ird level DWT and obtain e sub bands. Divide e LH 3 sub band into N number of blocks. Then, Compute e folloing. (a) block. (b) MWC N N (5) denotes e imum coefficient of N Mean block = N block excluding avg (6) avg is e average coefficient value of e Detect e atermark bit using e folloing detection rule for all to N fitness function. The fitness function is formed by combining e to metrics as follos. fit l PSNR l P p k, l k k () l denotes GA generation number, p denotes e total number of attacks used in e optimization process, represents value i attack k and eighting factor for. 4 Experimental Results, k l k represents Three different cover images are used for experimentation. They are Lena, Peppers and Barbara (5 5 pixels, 8 bits/pixel) hich are shon in Fig. (a), Fig. (b) and Fig. (c) respectively. MATLAB 7.0 and Checkmark. are used for testing e robustness of e proposed scheme. To dimensional DWT i Haar avelet filters is used. Genetic Algorim (GA) i a population size of 0 chromosomes, a crossover rate of 0.8 and a Gaussian mutation function (i a scale.0 and shrink.0) are used. Watermark bit=, if ( t 4 is e scaling factor, t 4 a ) > = sec = 0, oerise (7) a =imum ( avg, k = k = / avg / In Eq. (7), Mean block, k, k ) (8) MWC (9) Mean block (0) sec represents e second imum value of e block. The parameter / scaling factor, t 4, is used to control e value of e. 3.3 Optimization of parameters using GA GA can be used for atermarking applications [6], based on e fact at effective atermarking has to conflicting requirements, PSNR and. These to requirements are related to each oer and erefore e atermarking algorim described above must be optimized. Optimization search space and e fitness function are described as follos. (a) Lena (b) Peppers (c) Barbara Fig. Cover Images of size 5 5 The peak signal-to-noise ratio (PSNR) is used to evaluate e quality beteen an attacked image and e original image. PSNR is defined as follos: PSNR 0log 0 db () M N f ( i, ) g( i, ) M N x y M and N are e height and id of e image, respectively. f(i, ) and g(i, ) are e pixel values located at coordinates (i, ) of e original image, and e attacked image, respectively. After extracting e atermark, e normalized correlation coefficient () is computed using e original atermark and e extracted atermark to udge e existence of e atermark and to measure e correctness of an extracted atermark. It is defined as Search space: The values of e four scaling factors (t, t, t 3 and t 4 ) are e key at, if chosen properly, ill result in optimal imperceptible and robust atermarking. It is e role of e GA to find such values, here e GA's search space must include all possible values for e four scaling factors. The fitness function: To common performance evaluation metrics, PSNR and, are combined to form e m i i n m n [ ( i, ) ][ ( i, ) ] (3) [ ( i, ) ] m n i [ ( i, ) ] Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 583

4 DOI: /iecs/v5i.7 m and n are e height and id of e atermark, respectively. The symbols ( i, ) and ( i, ) are e bits located at e coordinates ( i, ) of e original atermark and e extracted atermark respectively. The symbols and are e values of e original atermark and e extracted atermark respectively. Genetic Algorim is executed to find e optimum values for e scaling factors of e proposed scheme. Scaling factors used in e proposed algorim are t, t, t 3 and t 4.Scaling factors can be adusted according to PSNR and requirement. The required values (target values for GA process) must be included in e fitness function ritten for GA. Assume at e required values for PSNR and are 4 and respectively. PSNR depends upon e scheme parameters t, t, and t 3. depends on t 4. But, PSNR and are not independent. Hence, it is not possible to fix e values for bo PSNR and. In addition, one can specify e eights as per requirements. As e required value of is very small in comparison i e required PSNR, a eight 0 is used for. [(4-PSNR) + 0(-)] is used as e fitness function in e GA process. GA minimizes e fitness function and produces e optimum values for PSNR,, and scaling factors. We can also specify one or more image attacks against hich robustness is required for e atermark. In ese experiments, attack i quality factor 40 is specified for GA. Table shos e results of GA i different cover images. Optimum values for PSNR, and scaling factors (scheme parameters) are shon, after observing GA results for five generations. Table Results of GA based optimization against e attack i QF=40. Cover image is Lena. Attack: -40 Fitness Function: (4-PSNR)+0(-) Initial range for Parameters: [0.-.0, , 0.-.0, ] Cover Image Fitness Value PSNR In db Lena Peppers Barbara Scaling Factors [t, t, t3, t4] [0.340, 0.796, , 0.606] [0.407, , 0.800, ] [0.8335, , 0.474, 0.440] Original atermark image is shon in Fig. (a). Fig. (b) shos e unattacked atermarked Lena. Fig. (c) shos e attacked (, quality factor 40) atermarked Lena. The extracted atermark is shon in Fig. (d). Scaling factors used for atermarking are t =0.340, t =0.796, t 3 = and t 4 = (a) (b) (c) (d) Fig. (a) Original Watermark Image (b) Watermarked Lena, PSNR=4.994 db (c) Attacked atermarked Lena i -40 attack, PSNR= db (d) Extracted atermark, =0.953 is one of e most frequently used formats in connection i e Internet and digital cameras. The quality factor is a number beteen 0 and 00 and associates a numerical value i a particular compression level. When e quality factor is decreased from 00, e image compression is improved, but e quality of e resulting image is significantly reduced. Varied quality factors are applied in e experiments, and e results are shon in Table for e Lena image. The proposed meod can detect e existence of a atermark rough quality factors greater an 0. The results sho at e value of is greater an 0.50 i quality factor greater an 5. Table of e atermark images extracted from attacked Lena atermarked images i different values for quality factors Quality Factor (QF) Oer attacks like median filter, Gaussian filter, average filter (lo pass filter), sharpening filter, histogram equalization scaling, cropping, salt and pepper noise etc., are also applied to e atermarked images and e corresponding results are shon in Table 3. The proposed meod can effectively resist all ose attacks. Table 3 of e atermark images extracted from Lena atermarked images i various oer attacks Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 584

5 DOI: /iecs/v5i.7 Type of Attack Median Filter 0.88 (3 x 3) Gaussian Filter (3 x 3) 0.9 Variance = 0.5 Average Filter 0.7 (3 3) Sharpening 0.8 filter Histogram Equalization 0.84 Scaling 50% Cropping 5% Salt & pepper noise (0.00) For average (lo pass) filtering attack, a 3x3 mask is used. The median filter is a non linear spatial filter hich is usually used to remove noise spikes from an image. The atermarked image is attacked by median filtering i a 3x3 mask. The cropping operation (lossy operation) deletes some portion of e image. The extracted atermark is still recognizable even after 5% of cropping. The atermarked image is attacked by salt and pepper noise i a noise density of The extracted atermark is still recognizable. The proposed meod is compared i Wang and Lin s [0], Li et al. s [], Lien and Lin s[] and Lin et al. [9] meods in terms of PSNR and (using e Lena as e cover image). Results of ose existing meods are found in [9]. Size of e atermark image (Logo) is 3 6 in ose meods. For comparison purpose, a atermark i e same size is embedded using GA based proposed meod and obtained e results. Comparison results are shon in Table 4 and in Fig. 3 in e graphical form. Performance of e proposed meod is better an e oer meods against compression and Gaussian filter attacks. But, is meod is slightly inferior in comparison i e meods in [9] and [] against sharpening and scaling attacks. The proposed meod can detect e existence of a atermark rough quality factors greater an 0. value obtained against (Quality factor 0) attack i e proposed meod is But, e value against e same attack for e existing meods is less an or equal to Similarly, proposed meod is better in terms of perceptual quality (PSNR) of e atermarked image. Optimum value obtained for PSNR i e proposed scheme is 4.9 db hen 3 6 size atermark is embedded. Optimization is performed against, average filter and high pass filters. Obtained parameter values are t =.70, t =.879, t 3 = and t 4 = Conclusions In is paper, a novel and an oblivious atermarking meod is proposed based on GA and using imum avelet coefficient modulation. Perceptual quality of e atermarked image is good and e atermark can effectively resist compression and various oer attacks like Gaussian filter, median filter, and average filter etc. Advantage of e proposed scheme is e effective use of GA to obtain e optimum response in terms of bo PSNR and. Experimental results sho at e performance of e scheme is better an e existing schemes in terms of e embedding capacity, PSNR and. In addition to copyright protection, e proposed scheme can also be applied to data hiding and image auentication. Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 585

6 DOI: /iecs/v5i.7 Table 4 Performance comparison of e proposed meod i e meods of Wang and Lin s [0], Li et al. s [], Lien and Lin s [] and Lin et al [9]. (Using Lena as e cover image and 3 6 Logo) S. NO Attacks Wang and Lin [0] (PSNR = 38. db) Li et al [] (PSNR= 40.6 db) Lien and Lin [] (PSNR= 4.54 db) Lin et al [9] (PSNR= 4.0 db) Proposed meod (PSNR= 4.9 db) (QF=0) (QF=0) (QF=30) (QF=70) (QF=90) Gaussian Filter Median Filter (3x3) Sharpening Filter Scaling (50%) NA NA NA Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 586

7 DOI: /iecs/v5i.7 (MTWC), SPIE s Annual Meeting-Application of Digital Image Processing, San Diego, pp , 997. [6] F.Davoine, Comparison of to avelet based image atermarking schemes, International Conference on Image Processing, Vancouver, pp , 000. [7] M.Hsieh, D.Tseng, and Y.Huang, Hiding digital atermarks using multiresolution avelet transform, IEEE Transactions on Industrial Electronics, Vol.48, No.5, pp , 00. (a) Wi Lin et al. [9] & Lien et al. [] [8] C.Temi, S.Choomchuay, and A.Lasakul, A robust image atermarking using multiresolution analysis of avelet, Proceedings of e IEEE ISCIT, pp.63 66, 005. [9] W.H.Lin, Y.R.Wang, S.J.Horng, T.W.Kao and Y.Pan, A blind atermarking meod using imum avelet coefficient quantization, Expert Systems i Applications, Vol.36, No.9, pp , Nov 009. (b) Wi Li et al. [] & Wang et al. [0] Fig. 3 Performance comparison of e proposed meod i e existing meods References [] P.Meerald and A.Uhl, A Survey of Wavelet-domain atermarking Algorims, Proceedings of e SPIE, Electronic Imaging, Security and Watermarking of Multimedia Contents III, pp , 00. [] R.Dugad, K.Ratakonda and N.Ahua, A ne aveletbased scheme for atermarking images, Proceedings of e IEEE ICIP, Chicago, pp.49 43, 998. [3] J.R.Kim and Y.S.Moon, A robust avelet-based digital atermarking using level-adaptive Thresholding, Proceedings of e IEEE ICIP, Kobe, pp. 6 30, 999. [4] S.G.Kon, S.W.Ban, I.S.Ha, et al., Highly reliable digital atermarking using successive sub band quantization and human visual system, Proceedings of IEEE ISIE, Pusan, pp.05 09, 00. [5] H.J.Wang and C.C.J.Kuo, High fidelity image compression i multireshold avelet coding [0] S.H.Wang and Y.P.Lin, Wavelet tree quantization for copyright protection atermarking, IEEE Trans. Image Processing, Vol.3, No., pp.54 65, 004. [] E.Li, H.Liang and X.Niu, An integer avelet based multiple logo-atermarking scheme, Proceedings of e IEEE WCICA, pp , 006. [] B.K.Lien and W.H.Lin, A atermarking meod based on imum distance avelet tree quantization, 9 Conf. Computer Vision, Graphics and Image Processing, pp.69 76, 006. [3] JH.Holland, Adaptation in natural and artificial systems: an introductory analysis i applications to biology, control and artificial intelligence, Cambridge, MA: MIT Press; 99. [4] [5] K.Ramananeyulu and K.Raaraesari, Wavelet Based Oblivious Image Watermarking Scheme Using Genetic Algorim, The Institution of Engineering and Technology (IET) Image Processing Journal, International Journal, Vol.6, No.4, pp , June 0. VeyselAslantas, A singular-value decompositionbased image atermarking using genetic algorim, International Journal of Electronics and Communications, (AEÜ), 6, pp , 008. Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 587

8 DOI: /iecs/v5i.7 [6] Ali Al-Ha and Aymen Abu-Errub, Performance Optimization of Discrete Wavelets Transform Based Image Watermarking Using Genetic Algorims, Journal of Computer Science, Vol.4, No.0, pp , ISSN , 008. Dr. K. Ramananeyulu, IJECS Volume 05 Issue February 06 Page No Page 588