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MEO based secured, robust, high capacity and perceptual quality image watermarking in DWTSVD domain
 Baisa L Gunjal^{1}Email author and
 Suresh N Mali^{2}
https://doi.org/10.1186/s400640150904z
© Gunjal and Mali; licensee Springer. 2015
 Received: 9 October 2014
 Accepted: 24 February 2015
 Published: 14 March 2015
Abstract
The aim of this paper is to present multiobjective evolutionary optimizer (MEO) based highly secured and strongly robust image watermarking technique using discrete wavelet transform (DWT) and singular value decomposition (SVD). Many researchers have failed to achieve optimization of perceptual quality and robustness with high capacity watermark embedding. Here, we achieved optimized peak signal to noise ratio (PSNR) and normalized correlation (NC) using MEO. Strong security is implemented through eight different security levels including watermark scrambling by FibonacciLucas transformation (FLT). Haar wavelet is selected for DWT decomposition to compare practical performance of wavelets from different wavelet families. The technique is nonblind and tested with cover images of size 512x512 and grey scale watermark of size 256x256. The achieved perceptual quality in terms of PSNR is 79.8611dBs for Lena, 87.8446 dBs for peppers and 93.2853 dBs for lake images by varying scale factor K1 from 1 to 5. All candidate images used for testing namely Lena, peppers and lake images show exact recovery of watermark giving NC equals to 1. The robustness is tested against variety of attacks on watermarked image. The experimental demonstration proved that proposed method gives NC more than 0.96 for majority of attacks under consideration. The performance evaluation of this technique is found superior to all existing hybrid image watermarking techniques under consideration.
Keywords
 MEO
 FLT
 Histogram
 Multiobjective
 Robustness
 Optimization and wavelet
Introduction
We are living in era of information technology with internet and mobile phones where billions of bits of multimedia data including images, audios, videos, digital libraries, online transactions are created, copied and transmitted in every fraction of second. Majority of transactions like railway, airplane reservations, shopping, banking, submitting tax returns are done online. The deployment of information and communication technology infrastructure is bringing revolution to health industry. The administration of Obama is offering $ 44,000 to $ 64,000 for electronic medical record (EMR) system based medical practices (Kamran & Farooq 2012). The unauthorized replication problem is critical issue. Digital image watermarking provides copyright protection by hiding appropriate ownership information in digital images. Thus, it is essentially required as valueadded technique for providing authentication features (Kamran & Farooq 2012). Robustness, imperceptibility, embedding capacity and security are four essential attributes those commonly determine quality of watermarking scheme (Urvoy et al. 2014). The main challenge in digital image watermarking is to achieve these parameters simultaneously as they conflict each other while achieving multiple level security. The spatial domain technique dual intermediate significant bit (DISB) is presented in (Mohammed et al. 2014) but it is vulnerable against simple attack. Most of the existing watermarking algorithms are developed in transform domain (Lai & Tsai 2010). In transform domain, watermark is inserted into transformed coefficients of image giving more information hiding capacity and more robustness against watermarking attacks because information can be spread out to entire image. The fragile watermarking techniques are given in (Piper & SafaviNaini 2013; SerraRuiz & Megias 2010). A blind watermarking technique for 3D images is presented in (Lin & Wu 2011). Spread spectrum (SS) watermarking based watermarking schemes are given in (Ehsan Nezhadarya et al. 2011; Kuribayashi 2014). In SS based watermarking, adding pseudo random noise like watermark into host signal is found robust in many attacks. In quantization based watermarking, set of features extracted from host signal are quantized so that each watermark bit is represented by quantized feature value. Reversible watermarking is special data hiding technique where original digital content can be completely restored after data extraction (Li et al. 2011; Coatrieux et al. 2009). Majority of watermarking techniques are invisible. The visible watermarking with exact recovery of cover image is presented in (Liu & Tsai 2010), however visible methods are used in limited application areas. Lossless data embedding methods are presented in (Shi et al. 2004; Celik et al. 2006). Secured image data transmission is required in applications such as teleradiology, telepathy, telecare, telesurgery, teleneurology demand safety and confidentiality of medical. The watermarking based security for telemedicine is provided in methods (Kamran & Farooq 2012; Bouslimi et al. 2012; Coatrieux et al. 2009). Many researchers have used specific transforms for implementing watermarking schemes. Most commonly used transforms are DFT, discrete Cosine transforms (DCT), discrete Laguerre transform (DLT), discrete Hadamard transform (DHT) and DWT. The DWT based watermarking methods are presented in (Wang et al. 2002; AbuErrub & AlHaj 2008; Cq et al. 2007; Aslantas et al. 2008; Senthil & Bhaskaran 2008). Fourier transform based methods are given in (Tsui et al. 2008; Tsui et al. 2006a,b). DWTSVD based watermarking algorithms are proposed in (Lai & Tsai 2010; Ganic & Eskicioglu 2004; Cq et al. 2007; Singh et al. 2012; Wang & Kim 2009). The Redundant DWTSVD based method is presented in (Lagzian et al. 2011). Contourlet transform and DCT are effectively combined using local complexity variations as given in (Azizi et al. 2013). Some researchers tried to optimize perceptual transparency and robustness under high payload scenario with the help of optimization techniques. Waveletbased genetic algorithm (GA) method (Ramanjaneyulu & Rajarajeswari 2012), redundant DWTSVD (RDWTSVD) based optimizer (Lagzian et al. 2011), DWTSVD based particle swarm optimizer (Aslantas et al. 2008) are example GA based techniques. The DCT has special energy compaction property. Most of visually significant information of the image is concentrated in just a few coefficients of the DCT. The DCT based methods are given in (Wei & Ngan 2009; Ahumada & Peterson 1992). Some of the researchers have done experimentation by combining DCT with other transforms. The combine DWTDCT approach is used in (Nikolaidis & Pitas 2003), DWTDCTSVD approach is used in (Sivavenkateswara et al. 2012). Image scrambling is used for secured watermark embedding. Different researchers have used various scrambling methods like Fibonacci transformation (Zou et al. 2004a), modified Fibonacci transform (Zou et al. 2004a), generalized Fibonacci transform (Zou et al. 2004b), Arnold transform (Umamageswari & Suresh 2013), grey code transformation (Zou et al. 2005), affine modular transform (Ehsan Nezhadarya et al. 2011). Other watermark scrambling based methods are presented in (Zou et al. 2005).
The most of researchers have been failed to develop effective watermarking techniques to fulfill four quality parameters simultaneously namely robustness, imperceptibility, high capacity watermark embedding and security. The novelty of proposed MEO based technique is to optimize imperceptibility and robustness in DWTSVD domain under high payload scenario with strong security provision.
Theory and mathematical background
Wavelet selection in DWT implementation
The use of multiobjective optimization using genetic algorithm is major part of this paper. Genetic algorithms are relatively slow as they are iterative in nature. Thus time complexity is critical issue of GA based algorithms. In GA process our objective is to select wavelet that will give better performance with less amount of time period.
Comparative computation time (unit seconds) with different wavelets
Wavelet  K1 = 10  K1 = 15  K1 = 20  K1 = 25 

Symlet (Sym12)  0.9984  0.9672  0.9828  1.0296 
Daubechies (db4)  0.8580  0.7488  0.8424  0.7332 
Daubechies (db8)  0.8736  0.9204  0.9672  0.8892 
Biorthogonal(bior4.4)  0.8424  0.9204  0.8112  0.9204 
Coiflet (coif5)  1.1544  1.1076  1.0296  1.1388 
Haar  0.7800  0.8580  0.7800  0.9204 
The PSNR between original cover image and noisy image is calculated. The execution time i.e. computation time is noted with different wavelets namely, Symlet (Sym12), Daubechies (db4 and db8), Biorthogonal (bior4.4), Coiflet (coif5) and Haar wavelet. It is found that Haar wavelet gives better PSNR with compared to other wavelets depending on value of K1.
The comparative computation time of this algorithm is minimum with Haar i.e.0.7800 for K1 = 10 and K1 = 20. Hence Haar wavelet has been selected for DWT decomposition in proposed methodology based on this performance. The Haar is simple, symmetric and orthogonal wavelet.
Selection of wavelet coefficients
The addition of watermark is equivalent to addition of noise to the cover image. Hence, the selection of coefficient for watermark embedding is very critical task. The images have maximum energy associated with low frequency subbands. Hence, watermark embedding in frequency subband (LL) should be avoided as it directly affects perceptual quality of image. The human naked eyes cannot detect modifications in high frequency coefficients. However high frequency subband(HH) contains edges and texture information of the image. In fact, high frequency coefficients are removed at the time of image compression which is normally applied before image transmission. Thus, rest of the choices is middle frequency subbands (HL and LH). But human visual system (HVS) is less sensitive in horizontal than vertical (Singh et al. 2012). Hence, HL has been selected for watermark embedding in proposed work.
SVD
 i.
Singular values correspond to brightness. The left singular and right singular vectors reflect geometric characteristics of image.
 ii.
The slight variations in singular values do not affect much visual perception.
 iii.
B and B _{r} (B rotated by certain degree) have same nonzero singular values.
 iv.
The row flipped B _{rf} and column flipped B _{cf} forms of B have same nonzero singular values.
 v.
If B _{e} is expanded by adding rows and columns of black pixels, the resulting B _{e} has same nonzero singular values of B.
FLT

Fibonacci12: 1, 2, 3, 5, 8, 13, 21,…..

Fibonacci23: 2, 3, 5, 8, 13, 21, 34 , ….

The Lucas series is nonperiodic series is given by,
Where, (X _{1}, Y _{1}) = {0,1,.....M1} are pixel coordinates of original image, T _{i} is the i ^{th} term of Fibonacci series S _{i} is the i ^{th} term of Lucas series, (i = 1, 2, 4, 5……….) (X _{2}, Y _{2}) are transformed coefficients after applying FibonacciLucas transform. M is size of original image.
As, two transforms are combined, resulting FibonacciLucas transform provides more security in watermark embedding phase.
GA based optimization

Step1: Initialize population size P, crossover rate Pc, mutation rate Pm, maximum generations N, scale factor K1.

Step2: Generate first generation of GA process using parameters in watermark embedding process. The different watermarked image is generated for each individual.

Step3: While generation ≤ N.
 i.
Find perceptual quality of watermarked image computing its PSNR
 ii.
Apply attack on watermarked image
 iii.
Call watermark extraction process
 iv.
Find robustness by computing normalized correlation between original watermark and extracted watermark.
 v.
Do evaluation based on fitness function where,
 vi.
Fitness Function = PSNR + K1*NC
 vii.
Select individuals with the best fitness values.
 viii.
Generate new population by performing crossover and mutation on the selected individuals.
 ix.
End while
 i.

Step4: Display PSNR, NC.
GA process starts with randomly selected population called first generation. The individual in population is called chromosome and all possible chromosomes constitute the population. The objective function also called fitness function evaluates the quality of each chromosome and it measures degree of goodness of candidate solution. The chromosomes where fitness value is high are selected for future generation. The initial population, selection, crossover and mutation are major stages of GA process. GA uses reproduction, crossover, and mutation repeatedly until either a predefined criterion is satisfied or numbers of iterations are completed.
MEO
Here, MEO in Matlab is used to optimize multiple objectives. Single objective optimization algorithms find single optimum solution for given fitness function. The goal of single objective optimization is to find global optima. While, minimizing one of the objective may not achieve desired effect on other. The aim of using MEO is to find optimum values of multiple objectives. In digital image watermarking, our goal is to achieve two optimized performance parameters perceptual transparency and robustness simultaneously against different attacks. We tried to achieve optimization of PSNR and NC for given scale factor (K1) with help of MEO.
Objective function 1

Where, Max_{I} is 255 for grey scale image,

Image1 (i, j) is pixel of original image,

Image2 (i, j) is pixel values of watermarked image,

M and N are the number of rows and columns both images.
Objective function 2
This is to evaluate the robustness of watermarked image. Robustness is measure of susceptibility of watermark against attempts to remove or destroy it by image attacks such as noise addition, noise filtering, scaling, translation, resizing, cropping, blurring, compression, rotation, collision attacks. NC measures the similarity and difference between original watermark and extracted watermark. Ideally it should be 1 but value 0.75 is acceptable.
Where, Watermark _{i} is original watermark, Watermark _{i} ' is extracted watermark, N is number of pixels in watermark.
Proposed MEO based methodology in DWTSVD domain
The proposed technique is implemented using eight different stages including cover object processing phase and watermark processing phase.
Implementation of eight stage security
Applying multistage security
Stages  Detail description of given stage 

Stage1:  Cover_Object is taken into Wavelet domain. 
Stage2:  As per given ‘State’ the Pn_Sequence of Watermark using ‘Key1’ is generated. 
Stage3:  Calculate AVG = average of Pn_Sequence 
Stage4:  Apply thresholding with ‘Key1’ to generate ‘K’ required for scrambling. 
Stage5:  Use FibonacciLucas Transform for scrambling Watermark with K. 
Stage6:  Apply Singular Value Decomposition, 
Stage7:  Apply ‘Embedding Formula’ with given scale factor K1. This K1 will be used for optimization in Step8. 
Stage8:  Apply MEO to optimize PSNR and NC using K1 
MEO based watermark embedding algorithm
Initially, pseudo random number sequence of watermark using Key1 at given state is generated. The average of pseudo random number sequence is computed. The key K is determined based on predefined threshold value T. This K is used for watermark scrambling using FibonacciLucas transform. Practically, sample periodicity of FibonacciLucas transform for M × N image with M = N is found as M. Here, for grey scale watermark images of size 256 × 256, the scrambling key 100 and descrambling key 156 are used as sample. K1 is scale factor which is used in MEO based watermark embedding algorithm. Here K, Key1 and K1 are integer values. The grey scale watermark, say W is scrambled to give scrambled watermark SW which is embedded in cover object by applying multiplicative rule. The MEO based watermark embedding algorithm is as follows,
Input: Cover_Object, Watermark W.

Step1: Read grey scale Cover_ Object of size MxN.

Step2: Decompose Cover_ Object using Haar wavelet, [LL,HL,LH,HH] = dwt2(Cover_Object,'Haar');

Step3: Apply SVD to HL subband of Cover_Object found in step 2: [U,S,V] = SVD(HL)

Step4: Read grey scale watermark W of size 256x256.

Step5: As per state of watermark W, generate Key1. Generate Pn_Sequence with ‘key1’. Calculate AVG = average of Pn_Sequence.

Step6: Calculate K in step 7 using predefined threshold T, predefined counter Count , FibonacciLucas periodicity P and Key1 generated in step 5.

Step7: Key1 + Count ≤ P, If AVG > T then K = P + Count else K = PCount.

Step8: Generate scrambled watermark SW by applying FibonacciLucas transform to Watermark with scrambling key K as per equation 11.

Step9: Perform embedding of watermark SW with Cover_Object by considering S found in step 3, SW found in step 8 and K1, S1 = S + K1*SW [U1,SS,V1] = SVD(S1)

Step10: Apply inverse SVD to get New_HL component as: CWI = U*SS*V', New_HL = CWI

Step11: Now apply one level inverse DWT with New_HL component to form Watermarked_Object as, Watermarked_Object = idwt2(LL,New_HL,LH,HH,'Haar',[M,N]);

Step12: Display Cover_Object, Watermarked_Object, PSNR and K1.
MEO based watermark extraction algorithm
The overall watermark extraction process is implemented using step1 through step10 as shown below.
Input: Watermarked_Object ,Cover_Object,

Step1: Read Watermarked_Object

Step2: Apply One level DWT to Watermarked_Object to have Recovered_HL1 component as, [LL,Recovered_HL,LH,HH] = dwt2(Watermarked_Object, 'Haar');

Step3: Apply SVD to Recovered_HL as, [UU,S2,VV] = SVD( Recovered_HL).

Step4: Read grey scale Cover_Object, size MxN.

Step5: Apply one level DWT to Cover_Object using Haar wavelet to get LL,HL,LH,HH subbands, [LL,HL,LH,HH] = dwt2(Cover_Object,'Haar');

Step6: Apply SVD to HL subband of cover image found in step 2: [U,S,V] = SVD(HL)

Step7: Find SN using component S2 in step 3 and components: U1 and V1 in step 9 of watermark embedding algorithm,

SNEW = U1*S2*V1'

Step8: Find Scrambled _Watermark using SN in step 7, S in step 6 and scale factor K1 used in watermark embedding algorithm as, Scrambled_Watermark = (SNEW S)/K1.

Step9: Apply FibonacciLucas transform to Scrambled_Watermark to find final Extracted_Watermark with key K.

Step10: Display Extracted_Watermark and NC.
Implementation of with MEO based algorithm
The MEO based watermark embedding algorithm and MEO based watermark extraction algorithm are used in Trial(K1) function, where K1 is scale factor passed to the function using multiobjective evolutionary tool in Matlab. The PSNR, NC and optimized K1 are displayed as output of this function. The algorithmic steps of ‘Trial’ function are given below.
Input: K1 passed through MEO based GA process.

Step1: Specify range of scale factor K1.

Step2: Specify GA parameters population size, reproduction rate, crossover rate and mutation rate.

Step3: Specify termination criteria by number of generations.

Step4: Write MEO based watermark embedding algorithm.

Step5: Apply attack on Watermarked_Object.

Step6: Write MEO based watermark extraction algorithm

Step7: Display parameters in step 8 at end of function as output.

Step8: Display y(1) = PSNR ,y(2) = NC.
Experiments and results
The proposed technique is implemented using Matlab version 8.0.0.7837 (R2012b) with multiobjective evolutionary optimizer tool. The experimentation is carried out on Intel(R) Core(TM) i3 processor of 2.10 GHz and 2GB RAM with 64 bit windows operating system.
Performance evaluation
Performance under varying number of generations with scale factor K1, PSNR for watermarked images (512 × 512 size) Lena, peppers and lake with NC for extracted watermark cameraman (256 × 256 size)
# generations  Test cases for scale factor(K1)  Watermarked image Lena  Watermarked image peppers  Watermarked image lake  

K1  PSNR  NC  K1  PSNR  NC  K1  PSNR  NC  
#5  Minimum K1  1.5443  79.8611  0.9728  1.4769  93.2853  0.9791  2.5227  87.8446  0.9773 
Average K1  2.1957  65.4463  0.9890  2.2085  73.9911  0.9822  3.1875  73.0528  0.9808  
Maximum K1  4.4482  51.9741  1  4.4798  55.2266  1  4.8300  59.5709  1  
#10  Minimum K1  1.5498  79.5746  0.9730  1.4769  87.2647  0.9796  2.5227  86.7532  0.9770 
Average K1  3.3328  65.8698  0.9877  2.2085  70.7811  0.9838  3.1875  73.0528  0.9808  
Maximum K1  3.3328  50.9538  1  4.4798  53.4861  1  4.8300  60.3296  1 
GA parameter setting in multiobjective optimizer tool in Matlab
Parameter setting  Case: 1  Case: 2 

No. of generations  5  10 
population size  15  15 
Reproduction rate  0.8  0.8 
Crossover rate  1.0  1.0 
Mutation rate  0.2  0.2 
Scale factor (K1)  1 to 5  1 to 5 
Robustness test
In addition to perceptual quality the proposed technique also achived robustness under variety of attacks.
The proposed method shows significant achievement of results in all attacks under consideration namely, median filtering 3X3 , average filtering 3X3 , Gaussian filter 3 × 3 with sigma = 0.5, wiener filter 5 × 5, salt and pepper with density 0.01, speckle Noise V = 0.01, Gaussian noise m = 0, v = 0.001, Poisson noise, rotation by 5(clockwise), rotation by5 (anticlockwise), gamma correction = 0.9, histogram equalization, scale by 2 attack, scale by 4 attack, shifting attacks namely translation [5 5], translation [10 10] and compression attack with qualify factor (Q.F.60%).
This testing has been carried out by noting score diversity plot and pare to front observations for individual attack in multiobjective evolutionary optimizer tool of Matlab.
Comparative performance analysis
Comparative\methods  Method (Lai & Tsai 2010 )  Method (Ganic & Eskicioglu 2004 )  Method (Azizi et al. 2013 )  Method (Ramanjaneyulu & Rajarajeswari 2012 )  Proposed method 

Domain Used  Hybrid DWTSVD  Hybrid DWTSVD  Hybrid contourlet DCT  GA based DWT  Hybrid DWTSVD 
Category (Blind/Non blind)  Non blind  Non blind  Blind  Blind  Non blind 
Type of Images (Grey/color)  Grey Scale  Grey Scale  Grey Scale  Grey Scale  Grey Scale 
Embedding Subband/Region  HL, LH subbands  LL,HL,LH HH used separately  Middle frequency  LH subband  HL subband 
Cover image with size  Lena 256 × 256  Lena 512 × 512  Peppers 512 × 512  Lena 512 × 512  Lena 512 × 512 
Watermark with size  Cameraman 128 × 128  Cameraman 128 × 128  Binary logo 32 × 32 size  Binary logo 64 × 64  Cameraman 256 × 256 
Attack type  Method (Azizi et al. 2013 )  Method (Ramanjaneyulu & Rajarajeswari 2012 )  Proposed (Best case) 

Median Filter (3 × 3)  0.960  0.8130  0.9972 
Gaussian Filter(3 × 3)    0.9069  0.9977 
Average Filter (3 × 3)    0.6884  0.9618 
Histogram Equalization  0.99  0.8880  0.9951 
Gaussian Noise (Density 0.001)  0.800  0.3922  0.9817 
Gamma Correction (0.9)  0.99  0.9983  0.9997 
Rotation (By 10° clockwise)    0.5695  0.9951 
Weiner Filtering (5 × 5)    0.8447  0.9728 
Salt and Pepper Noise(0.01)  0.930  0.9263  0.9817 
Resizing (50%)  0.960  0.6700  0.9977 
Compression (Q.F.60%)  0.816  0.9375  0.9789 
Attack type  Method (Lai & Tsai 2010 )  Method (Ganic & Eskicioglu 2004 )  Proposed (Best case) 

Cropping Attack (50%)  0.9843  0.7063  0.9872 
Rotation Attack (10° clockwise)  0.9897  0.9091  0.9951 
Gaussian Noise (Density 0.001)  0.9756  0.9377  0.9817 
Average Filtering Attack (3 × 3)  0.9597  0.7047  0.9618 
Compression Attack (Q.F.60%)  0.9772  0.9226  0.9789 
Histogram Equalization  0.9890  0.9700  0.9951 
Gamma Correction(0.9)  0.9994  0.9989  0.9997 
Contrast Adjustment (Histogram method)  0.9958  0.9759  0.9978 
Where, start _{time} is the time recorded at the beginning of algorithm execution and cpu _{time} is time recorded at end of the algorithm execution.
We ran our technique with number of generations as 1, population size as 15, reproduction rate as 0.8, crossover rate as 1.0, mutation rate as 0.2, scale factor K1 as 1.5443, cover image Lena of size 512 × 512, watermark cameraman of size 256 × 256.
The experimental results clearly show that proposed technique is faster with compared to method (Lai & Tsai 2010), method (Ganic & Eskicioglu 2004), method (Azizi et al. 2013) and method (Ramanjaneyulu & Rajarajeswari 2012).
 i.
The proposed technique is more robust than method (Lai & Tsai 2010), method (Ganic & Eskicioglu 2004), method (Azizi et al. 2013) and method (Ramanjaneyulu & Rajarajeswari 2012) for all attacks under considerations in HL subband.
 ii.
The proposed technique got significant achievement in perceptual quality than method (Lai & Tsai 2010), method (Ganic & Eskicioglu 2004), method (Azizi et al. 2013) and method (Ramanjaneyulu & Rajarajeswari 2012).
 iii.
The proposed method supports high capacity watermark embedding compared to method (Lai & Tsai 2010), method (Ganic & Eskicioglu 2004), method (Azizi et al. 2013) and method (Ramanjaneyulu & Rajarajeswari 2012).
 iv.
Experimentation is carried out for all minimum, average and maximum values of scale factor K1 for standard candidate images under test to know worst case, average case and best case performance of proposed method.
 v.
The majority of existing DWTbased image watermarking techniques are less robust to rotation and translation attacks. The proposed technique shows robustness towards rotation as well as translation attacks.
 vi.
The proposed technique is faster than method in (Lai & Tsai 2010), method in (Ganic & Eskicioglu 2004), method in (Azizi et al. 2013) and method in (Ramanjaneyulu & Rajarajeswari 2012).
 vii.
The proposed method is implemented through eight stages of security including FLT for watermark scrambling.
Conclusions
Existing GA based techniques are relatively slow. The DWT decomposition with Haar wavelet gives better PSNR with reduced computation time compared to DWT decomposition by Symlet, db4 and db8, bior4.4 and coif5. Hence, Haar wavelet is selected for DWT decomposition to achieve better performance. We achieved improvement of quality parameters with number of generations as 5 and 10. The proposed technique achieved normalized correlation as 1 for all cover images indicating exact recovery of watermark. We got PSNR 79.8611 for Lena, 87.8446 for peppers and 93.2853 for lake images when scale factor K1 was varied from 1 to 5. The proposed technique is compared to existing methods under consideration namely, method (Lai & Tsai 2010), method (Ganic & Eskicioglu 2004), method (Azizi et al. 2013) and method (Ramanjaneyulu & Rajarajeswari 2012) and found robust against variety of attacks. The technique supports high capacity hiding and perceptually superior to method (Lai & Tsai 2010), method (Ganic & Eskicioglu 2004), method (Azizi et al. 2013) and method (Ramanjaneyulu & Rajarajeswari 2012). As FibonacciLucas transformation is used, it is more secured with compared to Arnold CAT map, modified Arnold transform, Fibonacci series or generalized Fibonacci series. This technique has provided with eight layer security. In SVD, we have used multiplicative rule to improve quality parameters, whereas most of existing SVD based methods have used additive rule while embedding watermark in cover image. Majority of the existing DWT based algorithms use either or all LL, HL, LH, HH subbands for watermark embedding. We carefully selected HL subband. This technique is found more robust for rotation and translation attacks, though existing DWT based methods are less robust to these attacks. The ISO JPEG 2000 compression standard replaced DCT by DWT which is used by us. Ultimately, we are following ISO standards in our implementation. This technique is flexible and can be easily extended for color image watermarking namely RGB, YUV, YIQ, YCgCb and LUV color spaces to hide watermark in one of more color planes in HL subbands. The underlying technique can be extended for video watermarking. This work is in progress.
Declarations
Declarations
Authors would like to thank to ‘Amrutvahini College of Engineering’, Sangamner, Maharashtra, ‘Sinhgad Institute of Technology and Science’, Pune and ‘Padmashree Dr. D.Y. Patil Institute of Engineering and Technology’, Pune, Maharashtra, India for technical support during this research work. The authors also acknowledge following copyright free online test image databases available at, http://decsai.ugr.es/cvg/dbimagenes/index.php; http://www.osirixviewer.com/Downloads.html.
Authors’ Affiliations
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