Improvement of Robustness and Perceptual Quality of Image ...information hiding techniques. Digital image ... Experimentation and result analysis including performance evaluation,

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Technovision-2014: 1st International Conference at SITS, Narhe, Pune on April 5-6, 2014

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International Journal of Electronics Communication and Computer EngineeringVolume 5, Issue (4) July, Technovision-2014, ISSN 2249–071X

Improvement of Robustness and Perceptual Quality ofImage Watermarking using Multi-Objective

Evolutionary OptimizerBaisa L. Gunjal

Amrutvahini College of Engineering,Sangamner, Ahmednagar, Maharashtra, India

Email: [email protected]

Suresh N. MaliPrincipal, Sinhgad Institute of Technology and Science,

Pune, Maharashtra, IndiaEmail: [email protected]

Abstract – This paper proposes optimized Discrete WaveletTransform (DWT) and Singular Value Decomposition (SVD)watermarking technique using multi-objective evolutionaryalgorithm using Fibonacci-Lucas transform. In any imagewatermarking technique, there is always challenge forresearcher to achieve perceptual quality and robustnesssimultaneously under high payload scenario because thesetwo quality metrics are inverse of each other. We achievedoptimized ‘Peak Signal to Noise Ratio’ (PSNR) and‘Normalized Correlation’ (NC) in DWT-SVD domainapplying seven different security levels including Fibonacci-Lucas transformation based watermark scrambling. Theselection of wavelet for decomposition of cover image is doneafter testing practical performance various wavelets. Thetechnique is non-blind and tested with 512x512 size coverimages and 256x256 size grey scale watermark. We couldachieve normalized correlation equals to 1 for all coverimages indicating exact recovery of watermark. We gotPSNR 79.8611 for Lena, 87.8446 for peppers and 93.2853 forlake images when scale factor K was varied between 1 to 5.The technique undergone 17 various attacks and achievedextracted watermark with normalized correlation more than0.99 for majority of attacks. The significant improvement inperformance metrics is found with compared to existingDWT-SVD based image watermarking techniques in theliterature under consideration.

Keywords – Fibonacci-Lucas Transform, Improvement,Multi-Objective, Optimizer.

I. INTRODUCTION

Internet and multimedia technologies have become ourdaily needs and it has become a common practice to createcopy, transmit and distribute digital data. Obviously, itleads to unauthorized replication problem. The multimediadata protection techniques include stenography techniques,watermarking techniques, encryption techniques and otherinformation hiding techniques. Digital imagewatermarking provides copyright protection to digitalimages by hiding important information to declare rightfulownership.

Robustness, perceptual transparency, capacity and blindwatermarking are four essential factors to determinequality of watermarking scheme [1]. Watermark should bedifficult to remove or destroy. Robustness is a measure ofimmunity of watermark against attempts to image

modification and manipulation like compression, filtering,rotation, scaling, collision attacks, resizing, cropping etc.Imperceptibility means quality of host image should not bedestroyed by presence of watermark. The watermarkingtechnique should embed majority of watermarkinformation. Blind watermarking implies extraction ofwatermark from watermarked image without originalimage because sometimes it is impossible to avail originalcover image. Watermarking algorithms can be classifiedon several criteria. As per embedding domain: i) SpatialDomain ii) Frequency/transform domain iii) Temporaldomain. According to Extractor: i) Blind ii) Non-blind Asper Human perception: i) Visible ii) Nonvisible a) Robustb) Fragile. According to application: i) Source Based ii)Application Based. Watermarking algorithm commonlyhas two parts: embedding algorithm and detection i.e.extraction algorithm. The remainder of the paper isorganized as follows: Section II gives literature review andsection-II focuses on mathematical background. In SectionIV, proposed DWT-SVD based methodology is presented.Experimentation and result analysis including performanceevaluation, robustness tests and comparative performanceanalysis is given in section V. The conclusion is drawn insection VI.

II. LITERATURE REVIEW

Early watermarking schemes were introduced in thespatial domain, where watermark is added by modifyingpixel values of host image. Least Significant Bit(LSB)insertion is example of spatial domain watermarking. Butsuch algorithms have low information hiding capacity,they can be easily discovered and quality of watermarkedimage and extracted watermark is not satisfactory as pixelintensities are directly changed in these algorithms. TheFrequency domain the watermark is inserted intotransformed coefficients of image giving more informationhiding capacity and more robustness against watermarkingattacks[2][3][4]. Watermarking in frequency domain ismore robust than watermarking in spatial domain becauseinformation can be spread out to entire image. Both visibleand invisible watermarking techniques are implementedeither in spatial; domain or transform domain. Varioustransforms like discrete Fourier transform (DFT), discrete

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Cosine transform (DCT), continuous Wavelettransform(CWT), discrete Wavelet transform (DWT),singular value decomposition(SVD) or combinations ofdifferent transform are applied to in transform domain toachieve robustness and perceptual transparency. Commonmethod used for visible image watermarking is compressdata of cover image and embed it with given payload intocover image [5] [6][7]. Another approach is to use spreadspectrum method to spread the payload on cover image.Reversible visible watermarking and lossless recovery oforiginal images is proposed in [8][9]. Visiblewatermarking for bitmap images is presented in [10],while blind invisible watermarking for grey scale imagesis presented in [11]. Many existing visible watermarkingtechniques use binary logo embedding. But, someorganizations and industries use color logos. Few existinginvisible watermarking techniques are implemented inspatial domain while most of them are in transformdomain. Transform domain image watermarkingtechniques are presented in [12-22]. In [23], adaptivemultiwavelet image watermarking is presented. Invisiblewatermarking techniques presented in [24] are resistant tovarious image attacks. DWTSVD based watermarkingalgorithms are proposed in [25], [26]-[29] and [30].Theperceptual transparency and robustness are challengingissues, while embedding high payload capacity watermarkin an image. Few researchers tried to optimize these twoparameters using optimization techniques. DWT-DCT-SVD based particle swarm optimization [27], RedundantDWT-SVD (RDWT-SVD) based optimization [29],Wavelet-based with genetic algorithm (GA) [31] andDWT-SVD based particle swarm optimizer [32] arepresented. The DCT has special ‘energy compaction’property i.e. most of visually significant information of theimage is concentrated in just a few coefficients of theDCT. The DCT based method is given in [33] and [34].Some of the researchers have done experimentation withcombine approaches of combine DWT-DCT approach isused in [35],while combine DWT-DCT-SVD approach isused in [36]. Image scrambling is implemented to increasesecurity levels. Many scrambling methods are used invarious watermarking techniques by different researchersfor scrambling. Fibonacci transformation, modifiedFibonacci transform, generalized Fibonacci transform[37], Arnold transform[38], grey code transformation[39],other watermark scrambling based methods are presentedin [40].

III. MATHEMATICAL BACKGROUND

A. Wavelet and Sub band SelectionWavelet Transform is efficiently implemented using

‘Digital Filters’. DWT has become researchers focus forwatermarking as DWT is very similar to theoretical modelof Human Visual System (HVS). ISO has developed andgeneralized still image compression standard JPEG2000

which substitutes DWT for DCT. DWT offers multi-resolution representation of image and DWT gives perfectreconstruction of decomposed image [11]. Discretewavelet can be represented as,

For dyadic wavelets a0=2 and b0=1, Hence we have,

Image itself is considered as two dimensional signals.When image is passed through series of low pass and highpass filters, DWT decomposes the image into sub bands ofdifferent resolutions. Decompositions can be done atdifferent DWT levels as shown in fig.1.

Fig.1. One Level DWT decomposition of Image

At level 1, DWT decomposes image into four non-overlapping multi-resolution sub bands: LLx(Approximate sub band), HLx (Horizontal subband), LHx(Vertical subband) and HHx (Diagonal Subband). Here,LLx is low frequency component whereas HLx, LHx andHHx are high frequency (detail) components. Maximumenergy of most natural images is concentrated into lowfrequency sub bands. Thus, adding watermark into lowfrequency sub band will directly degrade image quality.Modification of HH1 coefficient can be considered forwatermark addition as human naked eyes cannot detect ifmodifications are made to these coefficients. But, HH1sub band includes edges and textures of the image. Themajor issue with low and high frequency sub bands isrobustness during attack handling. If we add watermark inLL1 and HH1, generally during image compression usingthresholding, zeros and very high coefficients areremoved. If we add watermark there, obviously, contentsof watermark will be destroyed in compression attack andit will result low normalized correlation between originalwatermark and extracted watermark. Thus, good areas forwatermark hiding are middle frequency sub bands HL1and LH1. But human visual system (HVS) is less sensitivein horizontal than vertical. Hence, we have selected onlyHL1 for watermark embedding.

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B. Singular Values by SVDSingular Values of the image gives very good stability.

When a small value is added, it does not result too muchvariation. Singular values possess intrinsic algebraic imageproperties [25]. Hence Singular Value decomposition inlinear algebra is used to solve many mathematicalproblems. Every real matrix A can be decomposed intoproduct of three matrices A= UΣVT, where U and V areorthogonal matrices such that, UUT =1 and VVT= 1 and Σis summation of diagonal entries λ1, λ2…..gives thesingular vectors of A. These diagonal entries are called asSingular Values of A and the decomposition is called as‘Singular Value Decomposition’. Thus we have,

where, r is rank of matrix A. Singular Values specifies theluminance of an image layer while corresponding pair ofsingular vectors specifies the geometry of the image layer.Singular values correspond to brightness and left singularas well as right singular vectors reflect geometriccharacteristics of image. Singular values of an image havevery good stability. Slight variations in singular values donot affect much visual perception. The largest of singularvalues changes very little for most common geometricattacks. Hence, SVD can be used as convenient tool forwatermarking in DWT domain.C. Scrambling by Fibonacci-Lucas Transform

Scrambling methods are used to transform meaningfuloriginal image into disorder and unsystematic pattern toform meaningless order to hide its real meaning. Variousscrambling methods including Fass Curve, Grey Code,Magic square, Arnold Transform, modified Arnoldtransform, Fibonacci-Q transform and generalizedFibonacci transforms are used by researchers instenographic and watermarking techniques. But, thesemethods do not provide enough security. Hence, hereFibonacci-Lucas transform is used to scramble watermarkbefore embedding into cover image. The Frenchmathematician Lucas proposed Lucas-series, which isextended version of Fibonacci series[37],[38].BasicFibonacci- Q transform is given by,

Where, (x, y) ϵ {0,1,.....N} are pixel coordinates oforiginal image ,n nx y are transformed coefficients afterapplying Fibonacci-Q transform. N is size of originalimage.The generalized Fibonacci- transform is given by,

The Lucas series is non-periodic series is given by,

By combining Lucas series with Fibonacci transform,Fibonacci-Lucas transform is given by,

where, (x, y) є {0, 1,.....N-1}are pixel coordinates oforiginal image. Fi is the ith term of Fibonacci series. Li isthe ith term of Lucas series, (i = 1, 2, 4, 5.. except 3). ,n nx y are transformed coefficients after applyingFibonacci-Lucas transform. N is size of original image.As, two transforms are combined, resulting Fibonacci-Lucas transform provides more securing required inwatermark embedding phase.D. Multi-objective Evolutionary Optimizer

Genetic algorithms were first developed by JohnHolland in 1970 and now they are widely used to solveproblems in various scientific and engineering applications[31]. In general GA starts with randomly selectedpopulation called first generation. Each individual inpopulation is called chromosome and all possiblechromosomes in list constitute population. In GA ‘fitnessfunction’ also called ‘objective function’ or ‘cost function’is used to evaluate quality of each chromosome and itmeasures goodness of candidate solution. Next generationwill be generated from some chromosomes where fitnessvalues are high. GA basically uses initial population,selection, crossover and mutation. Reproduction,crossover, mutation are three basic operators usedrepeatedly until either predefined criteria is satisfied ornumber of iterations are completed. Here, we have usedmulti-objective evolutionary algorithm to implementgenetic algorithm to optimize multiple objectives. Singleobjective optimization algorithms find single optimumsolution for given fitness function. The goal of singleobjective optimization is to find global optima. While,minimizing one of the objective may not achieve desiredeffect on other. The aim of using multiobjectiveevolutionary algorithm is to find optimum values ofmultiple objectives. In digital image watermarking, ourgoal is to achieve two optimized performance parameters:perceptual transparency and robustness against differentattacks simultaneously. We tried to achieve with help ofmulti-objective evolutionary algorithm. Our objective is tofind optimum values of PSNR and NC for given scalefactor K.First Objective Function: PSNR for perceptualquality::

Perceptual transparency is perceived quality of imagewhich should not be destroyed by presence of watermark.The quality of watermarked image is measured by PSNR.The bigger PSNR implies better perceptual quality ofwatermarked image. The perceptual transparency ismeasured with PSNR. PSNR between two grey scaleimages ( , )X i j and ( , )Y i j given by [1], [30], [31],

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Where, iMax is 255 for grey scale image.Mean Square Error (MSE) between two grey scale images

( , )X i j and ( , )Y i j is given by,

where, M and N are the number of rows and columns bothimages, ( , )X i j is pixel of original image ( , )Y i j is pixelvalues of watermarked image.Second Objective Function: NC for robustness::

Robustness is measure of susceptibility of watermarkagainst attempts to remove or destroy it by image attackssuch as noise addition, filtering, blurring, compression,rotation, scaling, translation, resizing, cropping, collisionattacks. It is measured in terms of NC. It measures thesimilarity and difference between original ‘watermark andextracted watermark. It’ value is generally 0 to 1. Ideally itshould be 1 but the value 0.75 is acceptable. NC is givenby,[35]

where, N is number of pixels in watermark, wi is originalwatermark, wi’ is extracted watermark Proposed DWT-DFT-SVD Based Methodology.

IV. PROPOSED METHODOLOGY

A. Watermark Embedding ProcessThis technique is implemented through seven stages for

enhancing security levels as shown in table-I. The step bystep details of watermark embedding process is shown intable-II.

Table I: Applying Multistage-Security

Table II: Algorithm: ‘Watermark Embedding Process’Input: Cover Image, Watermark W.Output: Watermarked_ImageSteps:1 Read grey scale ‘ Cover_Image’, size MxN.2 Apply one level DWT to ‘ Cover_Image’ using

‘haar’ wavelet to get LL1,HL1,LH1,HH1 subbands:[LL1,HL1,LH1,HH1]= dwt2(cover_object,'haar');

3 Apply SVD to HL1 sub band of cover image foundin step 2:[U,S,V]=SVD(HL1)

4 Read 256x256 grey scale watermark ‘W’.5 Generate Pn_sequence with ‘key1’ and

Calculate AVG= average of Pn_sequence. Here,‘key1’ is generated as per ‘state’ of ‘watermark’.

6 Use predefined Threshold ‘T’, Predefined counter’count’ , Fibonacci-Lucas Periodicity ‘P’ and ‘key1’found in step 5to give ‘key’ in step 7.

7 Key1+Count≤P,If AVG> T then Key=P+Countelse Key=P-Count

8 Apply ‘Fibonacci-Lucas Transform’ to ‘W’ withabove ‘key’ and using equations 10-12 to givescrambled watermark ‘W1’.

9 Perform Embedding of watermark ‘W1’ with‘Cover_Image’ considering S found in step 3, ‘W1’found in step 8 and scale factor K :S1=S+K*W1[U1,SS,V1]=SVD(S1

10 Apply inverse SVD to get New_HL1 componentas:CWI=U*SS*V'New_HL1=CWI

11 Now apply one level inverse DWT with‘New_HL1’ component to form‘Watermarked_Image’ as:Watermarked_Image =idwt2(LL1,New_HL1,LH1,HH1,'haar',[M,N]);

12 Display:‘Cover_Image’, ‘Watermarked_Image’,PSNR and scale factor ‘K’.

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Table III: Algorithm: ‘Watermark Extraction Process’

Table IV: Testing using Multi-objective GeneticAlgorithm

B. Watermark Extraction ProcessThe overall ‘watermark extraction process’ is

implemented using step1 through step 10 as given in table-III. The ‘embedding and extraction algorithms’ are calledin function ‘Trial (K) to include ‘multi-objectiveevolutionary algorithm’ keeping scale factor (K) flexibleas shown in table-IV. Practically, sample periodicity of‘Fibonacci-Lucas transform’ for MxN images with M=Nis found as M. Here, as sample, for 256x256 watermarkimages, we used scrambling key 100 and descramblingkey 156.

V. EXPERIMENTATION AND RESULT ANALYSIS

The proposed technique is implemented partially usingJava Netbeans IDE version 7.3[41],[42] and majority partin Matlab with multi-objective evolutionary algorithm[version 8.0.0.7837(R2012b). The experimentation iscarried out to evaluate imperceptibility, robustness withhigh capacity watermark embedding. The cover images of512x512 size and grey scale watermark images of256x256 from standard online database [43] are used forexperimentation.A. Performance Evaluation

The performance evolution is done using parametersgiven in equations 8, 9 and 10.The technique is tested withtwo different cases: namely case:1 and case 2 varyingvarious statistical parameters under consideration.Case 1: Number of generation=5::Population size=15, Reproduction rate: 0.8, crossover rate:1.0, Mutation rate:0.2, Bounds of Scale factor K= 1 to 5.

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Fig.2. Output with number of generations equal to 5:a) cover image Lena and watermark cameraman

b) cover image peppers & watermark cameramanc) cover image Lake and watermark cameraman

The tests are carried out for minimum, average andmaximum values of scale factor K with sample imageslena, peppers and lake. The PSNR was varied from51.9741to 79.8611 and NC varied from0.9728 to 1 forlena image.The PSNR varied from 55.2266 to 93.2853 andNC varied between 0.9791 to 1 for peppers image.Similarly, PSNR varied from59.5909 to 87.8446 and NCvaried from 0.9773to 1 for Lake image. The output forminimum, average and maximum i.e. best case, worst caseand average cases respectively is shown in fig. 2.Case 2: Number of generation=10::Population, size=15, reproduction rate: 0.8, crossover rate:1.0, mutation rate:0.2, Bounds of scale factor K= 1 to 5.

Fig.3. Output with generations equal to 10:

a) cover image Lena and watermark cameramanb) cover image peppers & watermark cameramanc) cover image Lake and watermark cameraman

The output for best case, worst case and average cases isshown in fig.3. The PSNR varied between 50.9538 to79.5746 and NC varied between 0.9730 to 1 for Lenaimage. The PSNR varied from53.4861 to 87.2647 and NCvaried from0.9796 to 1 for pepper image. Similarly, forLake Image, PSNR varied from 60.3296 to 86.7532, NCvaried from 0.9770 to 1. The value of scale factor is notedfor every recording. From Fig.2 and Fig.3, it is clear thatPeppers image gives better performance with compared toLake and Lena image.B. Robustness Tests

Fig.4. Watermarked Image ‘Lena’ under various attacks:(a)No attack (b) Median Filtering 3x3 (c) Average

Filtering 3x3 (d) Gaussian Filtering 3x3 (e) Weiner Filter5x5 (f) Pepper & Salt 0.001 (g) Speckle Noise V=0.01 (h)

Gaussian Noise m=0, v=0.001 (i) Poisson Noise (j)Rotation by -30 (k) Rotation by +30 (l) Gamma Correction

0.9 (m) Scaling by 2 (n) Histogram Equalization (o)Translation by [20 20] (p)Compression

Here, Lena image of 512x512 sizes after embeddingwatermark cameraman of 256 x256 sizes is tested undervarious attacks. The result after specific individual attackon watermarked image Lena is shown in fig.4.It isobserved that for Lena image, PSNR=51.9741 and NC=1at scale factor K=4.4482, indicating exact recovery ofwatermark. The tests are carried out for 17 different

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attacks and NC varied between 0.9789 to 0.9977 as shownin fig.5.C. Comparative Performance Analysis

Fig.5. Handlinking attacks on watermarked image ‘Lena’of 512x512 size: a) No Attack b) Median Filtering 3X3

c) Average Filtering 3X3 d)Gaussian Filter 3x3 sigma=0.5e)Wiener Filter 5x5 f)salt & pepper with density 0.01g)Speckle Noise V=0.01 h)Gaussian Noise m=0; v=0.001i)Poisson Noise j) Rotation by 4(Clockwise) k) Rotation

by-5 (Anti-clockwise)l)Gamma Correction=0.9 m)Histogram Equalization n) Scale by 2 Attack o)Scale by 4

Attack p) Shifting Attack: Translation[5 5] q)ShiftingAttack: Translation [10 10]r)Compression Attack

The proposed technique is compared with four existingDWTSVD based methods and our proposed techniquefound robust under all attacks compared to method [4],method [25], method [30] and method [31] as shown Fig6-a and fib.6-b. Also, proposed technique is foundperceptually superior than method [4], method [25],method [30] and method [31]. The comparative graphicalrepresentation of robustness and perceptual quality is alsoshown in Fig.6-c.

(a)

(b)

Fig.6. Comparison of proposed method witha) Robustness of method[4] and method[25]

b) Robustness of method[30] and method[31]c) Imperceptibility of methods [4],[25],[30],[31]

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VI. CONCLUSION

We present optimized DWT-SVD domain andFibonacci-Lucas transform based image watermarkingtechnique using multi-objective evolutionary algorithm.We tried to improve quality parameters with minimumnumber of generations. The proposed technique achievednormalized correlation equals to 1 for all cover imagesindicating exact recovery of watermark. We got PSNR79.8611 for Lena, 87.8446 for peppers and 93.2853 forlake images when scale factor K was varied between 1 to5. Our technique found strongly robust for majority ofattacks with compared to existing methods underconsideration i.e. method [4], method[25], method [30]and method [31]. As Fibonacci-Lucas transformation isused, it is more secured with compared to only ArnoldCAT map, Modified Arnold Transform, only FibonacciSeries or generalized Fibonacci series. In SVD, we haveused multiplicative rule to improve quality parameters,whereas most of existing SVD methods have used additiverule while embedding watermark in cover image. Most ofthe existing algorithms use either or all LL,HL,LH, HHsub bands for watermark embedding. But, we carefullyselected HL sub band. The ISO JPEG 2000 compressionstandard replaced DCT by DWT and our technique isusing DWT. Ultimately, we are following ISO standardsin our implementation. Existing GA based techniques arerelatively slow. Hence, first, we did efficient waveletselection so that given algorithm will be faster. Thistechnique is flexible and can be easily extended for colorimage watermarking for RGB, YUV, YIQ, YCgCb andLUV color spaces to hide watermark in one of more colorplanes in HL sub bands. This work is in progress. Theunderlying technique can also be extended for videowatermarking.

ACKNOWLEDGMENTS

We are thankful to ‘Amrutvahini College of Engineering(AVCOE), Sangamner, A’nagar’, ‘Sinhgad Institute ofTechnology and Science (SITS), Narhe, Pune’,‘Padmashree Dr.D.Y. Patil Institute of Engineering andTechnology(DYPIET), Pune’ and ‘Board of Colleges andUniversity Development(BCUD), University of Pune' fortechnical support throughout this research work.

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[41] http://www.netbeans.org/[42] www.java2s.com[43] StirMark Image Database 2.0.” [Online]. Available:

http://www.petitcolas.net/fabien/watermarking/image_database/index

AUTHOR’S PROFILE

Baisa L. Gunjalis pursuing Ph.D. in University of Pune and workingin Amrutvahini College of Engineering Sangamner,A’nagar, MS, India. She has 15 years teachingexperience and having more than 15 Internationaljournals and conference publications including IEEEComputer society, ACM etc. She is recipient of

‘Lady Engineer Award:2012from ‘Institution of Engineers’ and ‘BestTeacher Award-2013’ from University of Pune, MS, India.Her areas ofinterest are image processing ,computer networking and databases.

Dr. Suresh N. Malihas completed his PhD and presently working asprincipal, Sinhgad Institute of Technology andScience, Narhe, Pune, India. He has written threetechnical books and published more than thirtypapers in many reputed national and internationaljournals and conferences including ACM journal,

CSIC etc. He is active member of ‘Board of Studies’ for ComputerEngineering in various universities in MS, India. He has also worked asMember of ‘Local Inquiry Committee’ on behalf of University of Pune,India. He is member of IEEE, life member of ISTE and his researchinterests are information security, data hiding, signal processing, digitalmultimedia communications and Steganography.

www.ijecce.org

http://www.netbeans.org/

www.java2s.com

http://www.petitcolas.net/fabien/watermarking/image_database/i

Documents

Improvement of Robustness and Perceptual Quality of Image ...information hiding techniques. Digital image ... Experimentation and result analysis including performance evaluation,