sujatha et al., international journal of advanced engineering...

Sujatha et al., International Journal of Advanced Engineering Technology E-ISSN 0976-3945

Int J Adv Engg Tech/Vol. VII/Issue II/April-June,2016/978-987

Research Paper

MULTI WAVELET TRANSFORMS AND OPPOSITIONAL PARTICLE SWARM OPTIMIZATION FOR AN EFFICIENT

REVERSIBLE IMAGE WATERMARKING 1T.Sujatha, 2Dr.K.Geetha

Address for Correspondence 1Assistant Professor, Department of Information Technology, Sri Krishna College of Engineering and Technology,

Coimbatore, Tamil Nadu, India 2Professor and Head, Department of Electrical and Electronics Engineering, Karpagam Institute of Technology,

Coimbatore, Tamil Nadu, India.

ABSTRACT Digital Watermarking has evolved as one of the latest technologies for digital media copyright protection. Watermarking of images can be done in many ways and one of the proposed algorithms for image watermarking is by utilizing hybrid multi-wavelet transform and Oppositional Particle Swarm Optimization (OPSO). In this paper, initially, the image is decomposed with the help of multi-wavelet transform which is the combination of Haar and Bi-orthogonal wavelets. After that the best band suitable for embedding process is selected with the help of maximum entropy calculation. The best position of the selected band is chosen based on the Oppositional Particle Swarm Optimization algorithm. After that the private image is embedded into the selected position of the image. Finally, the inverse multi wavelet transform is applied to the image to get back the watermarked image. The same process is repeated to the extraction process. The performance of the proposed image watermarking scheme is analyzed through various constraints such as the Normalize Correlation (NC) and the Peak Signal to Noise Ratio (PSNR). The proposed scheme maintains the embedding quality with an average PSNR value of 58 dB. Keywords: - Watermarking, multi-wavelet transform, embedding, extraction, oppositional particle swarm optimization,

entropy, Secrete image.

1. INTRODUCTION Transferring secure information was a challenging task before the invention of steganography and cryptography. Therefore it was a difficult task to achieve secure communication environment. Hackers tend to change the original application either by modifying it or by using the same application to make profits without giving credit to the owner. Hence, protection techniques have to be efficient, robust and unique to restrict malicious users. Hence the development of the new concept called Watermarking was emerged. Digital Watermarking describes methods and technologies that conceal the information. This includes a number or text, in digital media, such as images, video or audio [1]. Digital watermarking techniques are classified into two- spatial domain and frequency domain. This is done according to the domain used for embedding watermark. Spatial domain based watermarking is focused on modifying the pixels of one or two randomly selected subsets of images. It directly loads raw data into the image pixels [2]. Spatial domain is not robust enough to withstand common image signal processing and attacks such as noise, filtering, and compression. It has chances of being easily destroyed by distortion. Frequency domain based watermarking, technique is also called transform domain. Here the values of certain frequencies are altered from their original to yet another form. Various research articles [8, 9, 10, 11, 12, 13] explore the watermark method. Some other methods hide the watermark pattern in the spatial domain [10, 12, and 13] yet there are others which embed the watermark pattern into the frequency domain [9, 11, and 12]. The most popular frequency domain techniques are Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) that are used for video watermarking. DCT is popularly used in watermarking techniques .It is used for identifying low frequency, high energy content in images [17]. DWT is a very attractive transform since it has efficient spatial localization, frequency spread and multi-resolution [9]. The combined DWT-DCT methods were implemented to embed monochrome

and/or grey scale watermark into Gray Scale Host Image [16]. In this paper the image watermarking based on the multi-wavelet transform and Oppositional Particle Swarm Optimization (OPSO) is presented. Here, at first the image is decomposed into four sub bands based on the multi-wavelet transform. After that the high frequency band is selected and entropy of each sub band is calculated. The maximum entropy band is selected and embedding position is selected that band based on the OPSO algorithm. Once the position is find out the secrete image bit is embedded into source image. Finally the watermarked image is obtained. In this techniqueany of the information present in the image is not lost. The rest of this paper is organized as follows: Section 2 describes a detail of the related works and section 3 describes the proposed image watermarking process. The experimental results are presented in Section 4. And Section 5 gives the conclusion of work. 2. RELATED WORKS Lot of researchers has explained image watermarking. Some of the papers are presented in the literature review; Hong Peng et al. [23] explained the image watermarking method in multi-wavelet domain which is based on support vector machines. The special frequency band and property of image in multi-wavelet domain were employed for the watermarking algorithm. Moreover, without knowledge of the original host video the random segmentation and reconstruction of embedded secrete data was done in Chitrasen and TanujaKashyap [24].During the process of embedding, secret data was embedded in individual video frames using the DWT’s frequency domains. Most of the past strategies were partly concerned with bringing lasting errors into the first information. But Mahmoud E. Farfoura et al. [25] guarantees one hundred percent recuperation of the primary database connection after the identification and validation of manager particular watermark. Sometimes Hackers tend to change the original application either by modifying it or by using the same application to make profits without giving credit to the



owner.Moreover, Transferring secure information was a challenging task for image steganography. Therefore it was a difficult task to achieve secure communication environment. S. Uma Maheswari, D. Jude Hemanth [26] explained the Frequency domain QR code based image steganography. Image steganography was defined as the art of hiding secret message onto the source image. Thus with the introduction of frequency domain steganography based information hiding technique using Fresnelet transform (FT), the Fresnelet coefficients of the Least Significant Bit (LSB) at high frequency sub bands were used to embed the QR coded secret message. But the good approach to steganography should possess two attributes: High stego-image quality and High embedding capacity. Sometimes it was not possible for this approach. Coltuc. D [27] took great effort to develop a low-deformation transform for forecast –error growth reversible watermarking. The transform recommended produced a reduced amount of deformation than the traditional forecast-error growth. This was for composite predictors like the median edge detector or the gradient-adapted predictor. Kuo-Liang Chung et al. [28] was also instrumental to devise a distortion decline method for histogram adaptation-based reversible data concealment. They moulded a watermark supplement method which decreased the deformation arising in HM-based RDH technique. A co-ordinated scrutiny for standard deformation ratio of the scheme suggested was also made available. Moreover, R. Naskar, R.S. Chakraborty [36] have explained the reversible digital image watermarking algorithm that predicts a pixel greyscale value exploiting its correlation with its neighboring pixels, and embeds watermark bits into the prediction errors. Their algorithm succeeds in providing high embedding capacity with very low distortion, without 'Multilayer

Embedding', hence reducing the computational burden compared with existing state-of-the-art algorithms. In [39] Hao-Tian et al., have explained the Reversible Image Data Hiding with Contrast Enhancement, which was used for trying to keep the PSNR value high and enhances the contrast of a host image to improve the visual quality. This method was losing some of the information of the images. The problem of the embedding capacity depends on signal that was the capacity of embedding on nature of the host signal and the block-wise dependence problems are explained in PoojaLoni et al. [40]. Moreover, to reduce the distortion of high payload watermarking scheme, a reversible watermarking based on optional prediction-error histogram modification was explained in Bo Ouet al. [41]. A reversible data hiding method was producing the problem of communicating pairs of peak points. 3. PROPOSED WATERMARKING METHODOLOGY The main intension of the proposed research is to develop an efficient reversible image watermarking scheme based on multi-wavelet transform and oppositional particle swarm optimization (OPSO). The system consists of mainly two processes such as embedding and extraction. In the embedding process, at first the source image is decomposed into different bands based on Haar wavelet transform. Of this low frequency band is selected and again the Bi-orthogonal wavelet transform is applied to decompose the image. After that the approximation band is neglected and entropy of other bands is computed. The band with maximum entropy is selected as the band and from that, the best position is found out using OPSO algorithm. Finally, the watermark image is imposed into the source image to obtain the watermarked image. The overall process of proposed system is illustrated below in Figure 3.1:

Figure 3.1: Overall diagram of the proposed watermarking system.

3.1 MULTI WAVELET TRANSFORM Wavelets are mathematical operations that divide information into various frequency segments, and analyse every segment with a firmness corresponding to its grade. They were developed independently from many areas such as image dispensation and image density. They are functions meeting specific statistical conditions and are employed in signifying data or other functions. In this work multi-wavelet transform is adapted to improve the PSNR value.

Multi wavelets have several advantages compared to single wavelets. A single wavelet cannot possess all the properties of orthogonal, symmetry, short support, and vanishing moments at the same time, but a multi-wavelet can. Here, at first the Haar wavelet transform is applied to the source image andthe four sub bands such as LL, LH, HL and HH are obtained. Among the four sub-bands three of them have high frequency information (LH, HL and HH) and one band having low frequency information (LL). After



the Haar wavelet transform, Bi-orthogonal wavelet transform is applied to the LL band in order to improve PSNR further. This also produced the four sub band such as LL, LH, HL and HH. Of this LL band is neglected and the HL, LH and HH high frequency bands are utilized for further processing. 3.2 SELECTION OF BAND Once the bands are separated, the band with maximum entropy is found out. Entropy is a measure of the improbability in a random variable. Entropy

)( N is a measure of unpredictability or information

content and is given by:

k

kkN PP 2log (1)

Here, kP is the probability that the difference

between two adjacent wavelet pixels is equal to k, and Log 2 is the base two logarithms. The procedure for selecting the band is explained below. Initially, the original image is resized to pre-defined standard size. Let the original image after

resizing be represented by G of size nm . After

resizing, 2D DWT using Bi-orthogonal transform is carried out to have approximation (LL) and detailed (LH, HL, HH) coefficient bands. From the detailed band, the best band is found using the entropy value. The entropy values are found out for the LH, HL and

HH bands and let it is represented by NHL

NLH , and

NHH respectively. Based on the entropy, the best

band ( X ) is selected for further processing. The band selection is based the following equations:

LHXandif NHH

NLH

NHL

NLH ),( (2)

HLXandif NHH

NHL

NLH

NHL ),( (3)

HHXandif NHL

NHH

NLH

NHH ),(

(4)

That is, the band having the maximum entropy value is taken as the best band and is selected for further processing; hence the above equations can be briefed up and represented as:

),,( NHH

NHL

NLH

NX MaximumE (5)

Here, NXE is the entropy of the selected band X .

3.3 OPTIMAL POSITION SELECTION Once the best band is selected, the next task is to find the optimal position where the embedding can take place. For this, OPSO algorithm is used. The Particle Swarm Optimization (PSO) algorithm is a simple evolutionary algorithm which differs from other evolutionary algorithms in which it motivates the simulation of social behavior. PSO has shown good performance in finding good solutions to optimization problem [29]. PSO is a population-based search algorithm and starts with an initial population of randomly generated solutions called particles [30]. Each particle in PSO has a position and a velocity. PSO remembers both the best position found by all particles and the best positions found by each particle in the search process. One problem found in the standard PSO is that it could easily fall into local optima in many optimization problems. Some research has been done to tackle this problem [31-33]. To overcome this problem, in this paper the opposition based PSO (OPSO) algorithm explains how toavoidthe premature convergences and allows OPSO to continue search for global optima by applying opposition-based learning [34-35]. The

basic concept of Opposition-based learning (OBL) is the consideration of an estimate and its corresponding opposite estimate simultaneously to approximate the current candidate solution. The proposed approach to population initialization used the opposition based method in which the population and its opposite population are taken as input. The fitness of both populations is evaluated and the best one is selected. The step by step process of proposed OPSO algorithm is explained below: Step 1: Solution initialization: Solution initialization is one of the important processes in the optimization algorithm. Here, the position of the sub-band is randomly selected. The selected bands have n number of pixels. Consider the solution nsssP ,...,, 21

is a solution in n-dimensional

space with Rsss n,...,, 21 and�� ∈ [��, ��]∀� ∈ {�}

nibas iii ,...,2,1, . The solution format is given

in following equation;

nDnn

D

D

i

sss

sss

sss

P

.......

.......

.......

21

22221

11211

(6)

Step 2: Calculation of opposite particle

Every particle iP has a unique opposite opiP

particle. The opposite particle nsssOP ,...,, 21 is

calculated based on the equation (7);

iiii sbas ni ,...,2,1 (7)

nDnn

D

D

i

sss

sss

sss

OP

.......

.......

.......

21

22221

11211

(8)

Step 3: Fitness calculation To evaluate the fitness of a solution, an objective function needs to be designed to quantify the performance of each individual. The fitness function is given in equation (9);

AFitness max (9)

PSNRNCA (10)

Normalized coefficient (NC) is calculated based on the original image and extracted image which is shown in equation (11);

1

0

21

0

*

2max

10

)(

log10h wW

x

W

yjkjk

hw

WW

WWEPSNR

(11)

Peak Signal to Noise Ration (PSNR) is calculated based on the original image and watermarked image which is shown in equation (12)

h w

h w

W

i

W

j

W

i

W

jw

kjW

kjEkjW

NC

0

2

0

0 0

)),((

),(),( (12)

Where, wW and hW is the width and height of the

watermarked image, jkW is the original image pixel

value at coordinate ),,( kj*

jkW is the watermarked

image pixel value at coordinate andkj ),(2maxE is

largest energy of the image pixels and ),( kjEw is

the extracted watermark image.



Based on the equation (9), the ultimate goal is the

maximization of an Objective function F which highly depends on the NC and PSNR. After calculating the fitness value the initial solution (Pi) and opposite solution (OPi) fitness value are checked. Based on the fitness value the solution is arranged into descending order. Step 4: Calculating local best and global best solution

Local best bestL is the best position that a particle has

visited yield the highest fitness value for that particle.

Global best bestG is the position where the best

fitness is achieved by any particle of the swarm evolved so far. Step 5: Velocity updation Momentum is a D-dimensional vector that determines the movement speed and direction of the particle. The velocity is updated by the following equation:

tibest

tiibest

ti

ti sGrandCSPrandCVwV

22111

(13)

Where;

iV Velocity of the particle i

iX Position of the particle i

ibestP Previous best particle for the thi particle

ibestG Global best particle found by all particles so

far

w Inertia factor

21 ,randrand Randomly generated value range of

[0,1]

21 ,CC Learning factor

Step 6: Position updation Each particle (potential solution) updates its position to walk in the solutions hyperspace in search of optimal solution. All the particles in a swarm walk speculatively for superlative positions and upgrade their positions using the following equation:

iii VSS 1 (14)

Step 7: Termination criteria The algorithm discontinues its execution only if maximum number of iterations is achieved and the particles which are holding the best fitness value is selected and it is given as the best position for embedding. 3.4 EMBEDDING PROCESS In this section the watermark embedding process is explained. The embedding is the process of securing information while transferring through networks. Here the private image jiS , is embedded into the

source image jiO , in order to reliable the

information from un-authorized person. The embedding process is shown below in Figure 3.2 and the process is explained in the following steps;

Figure 3.2: Overall diagram of the embedding process

Step1:Let us acknowledge the source image jiO ,

size of NM . At first the source image jiO ,

using Haar wavelet transform is decomposed; it will separate the image into four sub bands such as LL, HL, LH and HH. The LL band consists of low frequency information; it will reflect approximately the original image and the high frequency information are present in the LL, HL and HH sub bands. Step 2:To improve the efficiency ofthe embedding process, the multi-wavelet transform is used. Therefore one more time the wavelet transform to the approximation band (LL) is applied. Here, Bi-orthogonal wavelet transforms to decompose the LL sub band. In this stage the four sub bands such as low

frequency band (LL) and high frequency bands (HL, LH, HH) is also obtained. Step 3: After the particular band from LL, HL and HH to do the embedding process is selected. Here, the entropy for each sub band and select the maximum entropy band for further processing is calculated. The band selection process is explained in section 3.2. Step 4: Once the particular band is selected, after that the embedding position of that band based on OPSO algorithm is selected. The position selection based on OPSO is deeply described in the section 3.3. Step 5: Consider the secrete image jiS , which is

imposed into the selected band. Before embedding process, the image is converted into vector, this



vector consist of 0’s and 1’s only. This is called as

watermark bit bW .

Step 6: Then the watermark bit bW is hidden in the

selected position of the high frequency band. The watermark bit is embedding to the image pixel, two cases are available. Case 1: If the watermark bit is “0” means A random value (R) is generated with the same size as that of the band selected )( X . The watermark

image is a binary image having pixel values of 0 or 1

and let the thk bit in the watermark image be

represented by kW where }1,0{kW . The

embedding is carried out in case when the watermark pixel happens to be zero. In this case, the original pixel value in the location is modified with the help of the generated random number. Suppose the original image pixel is represented by

iS , then the

embedding process can be defined by:

)( RkSS ii (15)

Case 2: If the watermark bit is “1” means

Here, the watermark bit 1bW means we no need

to change any pixel value. We keep the pixel value as it is.

ii SS (16)

Step 7: After embedding process the inverse multi-wavelet transform is applied to obtain the watermark image jiW , and estimate the PSNR and

embedding capacity of performance measures. 3.5 EXTRACTION PROCESS During the extraction process, an operation reverse to the embedding operation is performed to abstract the

concealed message jiS , from a watermark image

jiW , and compare it to the original image jiI , .

In order to discover the original image jiI , , in the

beginning, the extraction algorithm performs the same operation as the embedding algorithm. The abstraction phase is narrated in the following steps: Step 1: At first, theHaar wavelet transform to the watermark image jiW , , is applied, it will produce

the four sub bands such as LL, LH, HL and HH. To improve the efficiency Bi-orthogonal wavelet transform to the approximation band (LL) is applied. Step 2: Once the band based on multi wavelet transform is separated, the particular band is selected. The entropy for each detailed band and the maximum entropy band is calculated and selected for further processing. Step 3:Subsequently, inputfrom the found out location )( iV and its correlation with the randomly

generated value (r) are taken. Half the correlation

value )( Vrc is compared with the mean value )( i and

correspondingly the watermark image bits )( i are

generated based on the condition given below:

1

)17(0

2

i

i

iVr

ELSE

THEN

cIF

By this method, all the watermark bits are abstracted from the watermarked image.

Step 4: Finally the inverse transform to the extracted

image is applied and the original image jiW E , is

obtained. 4. RESULT AND DISCUSSION: The results of this proposed methodology are offered in this part. In this paper, the proposed MWT – OPSO image watermarking approach is applied effectively for the images. 4.1 EVALUATION MATRICES The evaluation cadets used are Peak Signal to Noise Ratio (PSNR) and Normalised Correlation (NC). The quality is evaluated by the use of PSNR criterion in between the original images with the watermarked images and the extracting fidelity is compared using the NC value with original watermark and the extracted watermark. PSNR is wormed to extentthe indivisibility of the enclosed watermark in carrier image. NC is used to measure the similarity between the derived watermark and the pioneer watermark. The definition of PSNR and NC is given in the following equations.

1

0

21

0

*

2max

10

)(

log10h wW

x

W

yjkjk

hw

WW

WWEPSNR

(18) Where,

wW and hW is the width and height of the

watermarked image,jkW is the original image pixel

value at coordinate ),,( kj *

jkW is the watermarked

image pixel value at coordinate andkj ),( 2maxE is

largest energy of the image pixels .

h w

h w

W

i

W

j

W

i

W

jw

kjW

kjEkjW

NC

0

2

0

0 0

)),((

),(),(

(19) Where, ),( kjEw

is the extracted watermark image.

4.2 EXPERIMENTAT RESULTS This section gives the experimental results obtained for the proposed technique. The results obtained for five different images are given below in Table 4.1. The simulation figures includes source image, secrete image, watermarked image and recovered image. Here, the input grey scale image in the size of 512× 512 is used, after applying multi-wavelet transform to the source image, four sub bands of image each of size 256 × 256 is obtained. Moreover, the watermark image size of 32 × 32 is adapted.The Figure 4.1 shows the output sub bands of the multi- wavelet transform (MWT) in Lena image. Here at first the haar wavelet transform to decompose the source image is applied; it separates the image into four sub bands such as LL, LH, HL and HH. After that again the Bi-orthogonal wavelet transform to the LL band which consists of low frequency information is applied.Here, also the four sub band is obtained and high frequency sub bands are used for further processing.



TABLE 4.1: EXPERIMENTAL OUTPUT OF PROPOSED IMAGE WATERMARKING Source image Secrete image Watermarked image Extracted image

Figure 4.1: Sub bands of multi-wavelet transform



Figure 4.2: The evolution curve of the fitness value on source image with the PSO and OPSO

Figure 4.2 shows the evolution curve of the fitness value of generation with the OPSO and the PSO. The fitness values of the optimal solutions are plotted for both the OPSO and the PSO using the same

parameters. From Figure 4.2, it can be seen that the OPSO can obtain Maximum fitness (PSNR+NC) value than the PSO. The maximum fitness value improves the quality of extracted source image.

Table 4.2: Performance of proposed approach using PSNR value (dB) and embedding capacity (bits) method Images File Format PSNR (db) Embedding capacity (bits)

Proposed method

MWT+OPSO

brain JPEG 59.8466 1024

Barbara JPEG 58.3261 1024 Bone JPEG 58.9483 1024

Soldiers JPEG 58.9972 1024 Lena JPEG 58.1942 1024

DB-95 JPEG 57.8657 1024 cameraman JPEG 58.5632 1024

Average 58.66 1024

Table 4.3: Performance of proposed approach using NC value and embedding capacity (bits) Method Images File Format NC Embedding capacity (bits)

Proposed method MWWT + OPSO

brain JPEG 0.92342 1024 Barbara JPEG 1 1024

Bone JPEG 0.9886 1024 Soldiers JPEG 1 1024

Lena JPEG 1 1024 DB-95 JPEG 1 1024 cameraman JPEG 1 1024

Average 0.9824 1024

The above Table 4.2 shows the performance of proposed approach using PSNR and embedding capacity. The maximum PSNR of 59.84 db and average embedding capacity is 1024 is obtained. The performance of the proposed approach is measured based on the PSNR value.The PSNR value is high means the quality of the imageretrieved isalso high. The PSNR can certainly measure the intensity difference between two images, and it really is well-known that it may perhaps don't identify the visual

perceptual quality of the image. The Table 4.3 shows the performance of proposed approach using NC value and embedding capacity (bits) of the image. Here, the average NC value of 0.9824 is retrieved. 4.4 ROBUSTNESS ANALYSIS The robustness of the proposed watermarking technique is evaluated with the aid of different attacks. The attacks employed are filters, noise, cropping and blurring.

Table 4.4: Evaluation Metrics Value under varying noise conditions Noise PSNR (o-w) PSNR (o-r) NC

Gaussian Noise 32.10979098 32.11141779 0.802854938 Local Variance Noise 32.10174593 32.1027628 0.59992284

Poisson Noise 31.11142361 31.11141779 0.802854938 Speckle Noise 30.10746301 31.11141779 0.802854938

Salt & Pepper Noise(0.1) 31.10937452 31.10936858 0.748649691 Salt & Pepper Noise(0.2) 32.10727379 32.10726759 0.695794753 Salt & Pepper Noise(0.3) 30.10528071 30.10527428 0.640432099 Salt & Pepper Noise(0.4) 30.10322309 30.10321684 0.595486111

Table 4.5: Evaluation Metrics Value under varying effects and parameters Effects PSNR (o-w) PSNR (o-r) NC

Cropping (100) 34.8249788 28.250356 0.984567901 Cropping (150) 32.0549112 27.549758 0.984567901 Cropping (200) 30.5994412 29.886068 0.984567901 Cropping (250) 28.67976245 28.7976245 0.984567901 Cropping (300) 31.858613 30. 85861 0.984567901

Blurring(Len = 1 Theta =10) 48.4719190 53. 8617702 0.984567901 Blurring(Len = 2 Theta =11) 45.450486 49. 85861099 0.790509259 Blurring(Len = 3 Theta =12) 46.42853742 47.5687785 0.486689815 Blurring(Len = 4 Theta =13) 47.90396717 44.498904 0.482445988



Table 4.6: performance of the PSNR and NC value using different attacks Attacks Measures Brain Barbara bone soldiers leena Without attack

PSNR 58.8666 58.3261 58.9483 58.9972 58.1942 NC 0.92342 1 0.9886 1 1

Filter attack PSNR 50.4394 40.7771 47.2434 44.863 43.9812

NC 0.993245 0.967 0.9876 0.9986 9976

Noise attack PSNR 29.1012 27.0306 26.8456 26.8146 27.9365

NC 0.9342 0.9877 0.9975 0.9876 0.9945 Cropping

attack PSNR 29.5055 27.8657 27.2771 28.0258 28.7727

NC 0.978 0.934 0.9768 0.9876 0.9987 Blurring

attack PSNR 47.4009 37.0417 45.5049 42.3387 41.0612

NC 0.9655 0.9756 0.97 0.965 0.978

In this section the robustness analysis using various attacks for the proposed approach is given. Here, the attacks are filters, noise and effects is used.The Table 4.4 shows the Evaluation Metrics Value under varying noise conditions. The noises considered are Gaussian noise, Variance noise, Speckle noise, Salt and Pepper noise. The techniques perform well under all noise conditions. The Table 4.5 shows the Evaluation Metrics Value under varying effects and parameters. From the table, the technique performed well is inferred with effects too. The Table 4.6 shows the performance of the PSNR and NC value using different attacks. 4.5 PERFORMANCE COMPARISON OF PROPOSED APPROACH The basic idea of our research is to image watermarking based on multi wavelet transforms and oppositional particle swarm optimization algorithm (MWT+OPSO).The main motivation of using this approach is the capability of MWT in yielding high

embedding capacity, more security and better quality of watermarked image. Here,the effectiveness of the proposed approach is proved;this proposed MWT+OPSO iscompared withWeighted Median Approach[36] and BWT&IMM [37] and DPEIRW [38].The above Table 4.7 shows the comparative analysis of proposed against existing approaches based on PSNR. Among the seven images the brain image only obtains the high PSNR value. Here, the maximum PSNR of 54 db for (O-E) and 56 db for (O-W) using chest image is obtained. Similarly,Table 4.6 shows the PSNR performance of various cases for proposed work. In this work, additionally multi-wavelet transform and OPSO algorithm is used. These two modifications are improving the PSNR value of proposed approach. Here, the maximum PSNR of 59.84 (O-E) db for using brain image is obtained. From the results it is clearly understandable that this proposed approach is having better attainment correlate to existing approach.

Table 4.7: Comparative analysis of proposed against existing based on NC

MWT + OPSO

BWT & IMM [37]

DPEIRW

[38]

Weighted

Median [36]

Brain MRI 0.92342 0.8543 0.987 0.9643

Barbara 1 0.8675 0.9743 0.9541

Chest 0.9886 0.8534 0.9743 0.9654

Soldier Image 1 0.8765 0.9789 0.9647

Lena 1 0.8835 0.9856 0.9786

Table 4.8: Comparative analysis of proposed against existing based on PSNR

MWT + OPSO

BWT & IMM [37]

DPEIRW

[38]

Weighted Median

[36]

Brain MRI 59.8466 31.6565 53.1919 41.853

Barbara 58.3261 29.7584 49.2935 38.7081

Chest 58.9483 30.4016 54.0593 43.405

Soldier Image 58.9972 30.1204 52.805 38.5477

Lena 58.1942 30.0248 49.8573 40.2169

4.6 COMPARISON WITH OTHER APPROACHES In this section, the PSNR value of this proposed work is comparedwith [36], [37], [38], [39], [40] and [41]. The methods proposed by [36], [37], [38], [39], [40] and [41] are the best known among existing schemes image embedding approaches. Furthermore, they

characterize the embedding capacity of the image. Therefore, to compare the performance of this proposed algorithm against that of these ones is chosen. The Table 4.9 shows the Comparative analysis of proposed against existing approach based on PSNR.

Table 4.9: Comparative analysis of proposed against existing approaches based on PSNR

Here, in the first work [37] reversible watermarking using Bi-orthogonal wavelet transform and importance measure model is used.In second work [38] reversible image watermarking based on dynamic prediction error is developed. Now, in this proposed work the reversible image watermarking based on the multi wavelet transform and OPSO algorithm to select the location of the embedding bit

is implemented. The pixel prediction based on medial based approach is explained in the [36]. In [39] Hao-Tian Wu et al., the concept of Reversible Image Data Hiding with Contrast Enhancement is explained. In [40] also they explained the Reversible Fragile Image Watermarking Scheme. Finally one more approach is used for comparison. The Reversible watermarking using optional prediction error histogram

Performance measure

MWT+OPSO BWT & IMM [37]

DPERRW [38]

Naskar et al [36]

Hao et al [39]

Pooja et al [40] Bo ou et al [41]

Average PSNR 59.8466 31.6565 54.0593 41.853 30.38 44.5869 37.1192



modification is explained in [41]. Table 10 shows the comparative analysis of proposed against existing approach based on PSNR. Here, our proposed approach achieves the maximum PSNR of 59.84 db which is 31.65 for using [37], 54.05 db for using [38], 41.85 db for using [36], 30.38 db for using [39], 44.58 db for using [40] and 37.11 db for using [41]. From the result part our proposed approach achieves the maximum PSNR of 59.84 db which is high compared to the all the existing approaches is clearly understood. 5. CONCLUSION The Frequency domains secrete image based reversible image watermarking employing the multi wavelet transform is proudly launched through this paper. In this novel approach, at first the image is decomposed based on the multi wavelet transform. After decomposition, the band based on the maximum entropy and embedding position is selected using OPSO algorithm. Once the ideal position is found, the secrete image is embedded into the source image. Finally, the watermarked image without losing any information of the image is obtained. The novel technique effectively preserved the watermarked image quality with a maximum PSNR of 59.84. The proposed scheme is imperceptible and robust against distinct attacks and has a good attainment correlated with existing approaches. REFERENCES

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