A Hybrid Technique of Blind Color Watermarking Employing RDWT and SVD

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A Hybrid Technique of Blind Color Watermarking Employing RDWT and SVD

Neha Sharma

Electronics and Telecommunication Engineering Bhilai Institute of Technology, Raipur

Arpita Shukla

Electronics and Telecommunication Engineering Bhilai Institute of Technology, Raipur

Abstract:- Robustness, security, imperceptibility are the basic requirements in today's scenario for protecting the authenticity of digital data from unauthorized sources. Digital watermarking is a remarkable technique to retain the original nature of digital data like images and videos from various interceptions. In the given text, color image is secured using invisible color watermarking by exploiting the intelligence of Redundant Discrete Wavelet Transform (RDWT) and Singular Value Decomposition (SVD) technique. A multi level decomposition of host as well as secure image using RDWT has been performed and moreover SVD is employed thus providing soundness and reliability to the work. Research results demonstrate and prove the robustness and liability of the defined scheme.

Keywords RDWT, SVD, watermarking

  1. INTRODUCTION

    As because of the growing use and easy access of internet services anyone can misuse the digital data and thus to provide security from threats and attacks and also for maintaining the digital right of a person or firm, embedding of watermark to the actual digital data is requisite. Various solutions are available in literature for securing digital multimedia from attacks out of which watermarking scheme has gained popularity.

    Watermarking is a technique of implanting confidential data to the genuine digital data thus enhancing the security and susceptibility and so is essential for providing information about the legitimate owner of the digital content. The secret key that is embedded can be some serial number, text, images, a firm logo and so on. Though watermarking method [19,20] for protecting digital multimedia is accepted and admired nowadays but it essential to highlight some of the issues related to watermarking technique like firstly, the watermark should not devalue the quality of original data and also it should not be visible for maintaining secrecy of its presence and secondly the nature of secret key used as watermark should be robust enough so that it cannot be harmed from normal image processing techniques but at the same time can be detected by the rightful owner of the digital content. The watermarking technique is majorly categorized as : 1) Blind and Non-Blind watermarking technique 2) Visible and Invisible watermarking techniques.

    In blind watermarking method [21] neither the original data nor any information about it is required whereas non blind method requires the original data to recover the encrypted watermark.

    Visible watermark is embedded such that it is clearly visible in the digital multimedia like several institutions and firms use their logo for proving ownership over digital content unlike invisible watermark which is embedded in such a way that it cannot be seen from naked eye and can be extracted by the owner for proving their copyright.

    Color of the image plays a significant role in watermarking. An image can be color, gray, or monochrome. Various existing techniques are based on gray images in which the original digital data and secure image both are gray and these methods cannot be directly outstretched for color images, because color image depends on both chrominance and brightness. Watermarking done on a color image offers more resistance to deliberate and accidental attacks.

    Digital watermarking can be embedded using spatial domain and Transform domain watermarking techniques [11]. These methods have some pros and cons associated with them and can be deployed based on their necessity.

    The spatial domain technique [2, 18] requires less number of mathematical computations and can be implemented faster with more content embedding capacity but is a flimsy and fragile in nature. It uses two to three Least Significant Bits for computation of recovery information and is also known as LSB technique. Spatial Domain technique can also be applied to single color of a colored image. This algorithm employs modification in the Least Significant Bit (LSB) of chosen pixels in the Originalimage and direct loading of the raw data into it i.e. replacing the least significant bits of Originalimage with the least significant bits of secure image. But this elementary technique is not robust in nature and can be easily destroyed through attacks though change in LSB does not degrade or reduce the quality of genuine image.

    Another robust technique used for embedding watermark is Transform domain method [1]. It is widely accepted and applied method. This method is proven to be more effective when compared with spatial domain technique in terms of achieving robustness and imperceptibility. Robustness is the property of defending various intentional as well as unintentional attacks and maintaining the quality and significance of extracted watermark image. Imperceptibility means the embedding of watermark should not degrade the quality of original digital content and is another important property to be taken into account while performing procedure of watermarking . In Transform domain technique the watermark is embedded in the spectral coefficients of original content. The commonly used Transform techniques for watermarking are Discrete Fourier Transform (DFT),

    Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Redundant Discrete Wavelet Transform

    Fig.1. Three level decomposition of host image.

    (RDWT), Discrete Hadamard transform and so on. Various watermarking techniques have been developed by researchers utilizing concepts of transform domains like DFT [11,12], DCT [13], DWT [14,15,22],IWT [24], RDWT

    [1,10,16,17,23,26] . Now a days hybrid scheme has been evolved which further enhances the security along with resolving issues related to robustness and imperceptibility of watermarking techniques. This hybridization is nothing but combination of above described transform domain methods with Singular Value Decomposition (SVD) and has gained popularity [1, 2]. In the past years, various watermarking schemes are designed for grayscale images. Either both the host as well as secure image is grayscale images [8, 9] or host is color but secure image is a grayscale image [3, 4]. A big challenge in the field of watermarking is embedding of color secure image into color Original image as mostly the literature works are done for gray scale images in the previous years. But recently several color image techniques have been developed that uses Pseudo random sequence, gray image, steganography, encryption, and binary data as watermark.

    A blind spatial domain technique combined with frequency domain is proposed in [2] which uses binary secure image for embedding into blue component of colored host image. A grayscale watermarking scheme employing DWT- SVD in host image for placing watermarks principal component is proposed in [4]. Another gray scale watermarking has been proposed in [9] utilizing the intelligence of ABC algorithm for optimization of fitness or scaling factor thereby increasing security. Ali et al. [8] has also proposed grayscale watermarking algorithm using hybrid IWT-SVD and also employs ABC algorithm for proper fitness factor selection. In [23] hybrid combination of IWT and SVD is presented for grayscale images where the host image is decomposed into level 1 sub bands by the use of IWT and secure images pixels are directly inserted into the SVD of decomposed host image. I [7] the properties of DCT, DWT, Arnold mapping, and error correcting code (Hamming code) are used for enhancing rigidity of color watermarking. Colored image is converted into YCbCr form and hamming codes are placed into the intensity component and this component is applied for watermarking. Blind watermarking of color images based on LU decomposition focusing to improve the watermark reliability and soundness with the help of Arnold transform and hash pseudorandom

    number is presented in [5]. In [2] Su et al. has discussed the concept of color image watermarking utilizing the concept of spatial domain 2D-DFT technique. RDWT-SVD based technique is employed in [10] for gray scale images. Another combination of RDWT and SVD is proposed in [26] for grayscale images, here secure image is embedded directly into RDWT of host image. For increasing robustness of scheme a self adaptive differential evolution (SADE) algorithm is proposed for proper selection of scaling factor. In [27] another color watermarking scheme is proposed, utilizing the concept of RDWT and SVD, here jumbling of secure image is done before embedding to increase the security of watermarked image. Sharma et al. in [1] has proposed hybrid watermarking scheme using RDWT combined with SVD. Here both host and secure images are color images. Both the images are scrambled using Arnold mapping and host image is decomposed into four sub bands using RDWT technique. Out of which the approximate sub band is used for applying SVD to obtain principal component (PC).

    A new hybrid approach is defined in this research paper which employs the use of multi level RDWT along with Singular Value Decomposition (SVD) for embedding non blind and invisible color watermark also known as secure image into a still color image know as Original image to validate the authentic owner. The main significance and contributions of research work are as shown below :

    1. Multi level decomposition of both host and secure image using RDWT technique for increasing the security of watermarked image.

    2. Performing SVD in the approximate band of both host and secure image. Using scaling factor for modifying the singular value of Original image thus generating robust watermarked image which further increases the security and also it is tedious for unauthentic user to extract the secure image.

    3. To subdue the tradeoff between robustness and imperceptibility even when the watermarked image goes through various intentional and unintentional attacks.

    The imperceptibility and robustness are measured qualitatively and quantitatively by computing PSNR and NC values for various malicious attacks. Also the result depicts adequacy and outperformance of the research work.

    Research work is arranged as : Section I is preliminaries, section II is proposed scheme, evaluation parameters are discussed in section III, section IV is experimental results and discussion ,conclusion is presented in section V.

  2. PRELIMINARIES

    1. Redundant Discrete Wavelet Transform

      A wavelet based transform imparts both frequency and spatial elucidation of an image unlike DFT and DCT based transforms, if a signal is embedded using wavelet it will affect image locally. Discrete Wavelet Transform is a mathematical approach to transform image pixels into wavelets, which are then employed for wavelet-based computing. As it is already established that DWT method is less vulnerable to attacks and has enhanced performance when compared with DFT and DCT approaches. But a major drawback of DWT is its shift variant nature. This shift variant nature of DWT is the result of up-down sampling of the sub-

      bands after filtering. Even if the signal has slight shift it will significantly change the transform coefficient, thus making

      DWT less robust for watermarking of digital content.

      Level 3 RDWT

      g(y)LL3

      g(y)LL3

      h(y)LH3

      h(y)LH3

      Level 2 RDWT g (x)

      h(y)LH2

      h(y)LH2

      Image

      f (x,y)

      Level 1 RDWT

      g(y)LL1

      h(y)LH1

      h(y)LH1

      g (x)

      g (x)

      h (x)

      g(y)LL2

      g(y)HL3

      g(y)HL3

      g(y)HL2

      g(y)HL2

      h (x)

      h(y)HH3

      h(y)HH3

      h(y)HH2

      h(y)HH2

      g(y)HL1

      g(y)HL1

      h (x)

      h(y)HH1

      h(y)HH1

      Fig. 2. Block diagram of 3 level RDWT.

      1. (b) (c)

      Fig. 3. Host Images (a) Lena, (b) Mandrill, (c) Peppers.

      (a) (b) (c) (d)

      Fig. 4. Colors secure images (a) Color Chips, (b) Splash, (c) Plane, (d) 8Color image.

      To overcome the demerits of DWT, Redundant Discrete wavelet Transform (RDWT) also called as Undecimated DWT, Over complete DWT, Shift Invariant DWT is gaining importance now-a-days [1,10,16,17,23,26]. The frame expansion in RDWT increases the attack handling capacity of host signal as a result of which the robustness and imperceptibility of the image is upgraded. RDWT does the multi resolution analysis of the image by breaking a single image into 4 sub bands of same dimension. These four sub bands are namely: Approximate or LL band, horizontal or LH band, Vertical or HL band, diagonal or HH band.

      Equation of RDWT

      1. RDWT analysis

      2. RDWT synthesis

        (3)

        1. Here, and represent low pass and high pass synthesis filter coefficients whereas and shows low pass and high pass analysis filter coefficients

          respectively. And are low band and high band of mth level coefficients.

    2. Singular Value Decomposition

    SVD [21, 25] is a factorization based numerical method used for analyzing symmetric matrix by decomposing the matrix into three rectangular matrices thus manifesting the intelligent and interesting factors of the original digital image. A digital image can also be represented and converted in matrix form where the elements show intensity values at different positions. SVD breaks down a matrix into three different matrices namely Left singular or unitary matrix (U), Singular matrix (S), Right singular matrix or complex unitary matrix (V). Suppose, D is the colored digital image of the order of m*n, then its SVD is represented by:

    (4)

    Where,

    U is m*m column orthogonal matrix and its column is Eigen vectors of AAT.

    V is n*n orthogonal matrix and its columns are Eigen vectors of ATA.

    Matrix and r min (m, n) and d1, d2 etc represent diagonal elements.

    U and V matrices carry the detailed and decomposed statistics of the image. Multiplication of U and V matrices is called as the Principal component (PC) of the image.

    (5)

    S is a diagonal matrix of order n*n where the elements are non negative real numbers arranged in descending order i.e. dndr+1 dr.. d2 d1. Here, r represents rank of the

    Host Image

    The singular matrix (S) element represent contribution of decomposed image layers in the final image formation and generally represents the brightness or intensity level of any image. Orthogonal matrices U and V follow the properties UUT = I and VT V = I where I is Identity matrix.

  3. PROPOSED SCHEME

    In a nutshell the proposed work comprises of multi level hybrid combination of two different technologies RDWT and SVD. Multi level decomposition of image has been done using transform domain RDWT technique as it can be seen in Fig.1. Both the host as well as secure image undergoes three level decomposition and then SVD is employed. As it is already mentioned above that the singular value contains the intensity information of an image and hence any change in singular value either due to modification or due to malicious attacks does not affect the image much. For embedding secure image in Original image, S matrix values of original image is altered using scaling factor () and singular values of watermark image. In this manner a robust watermarked image is created.

    1. Embedding scheme

      Embedding scheme block diagram is shown in Fig.5. mbedding of watermark is organized as: (a) pre-processing phase (b) embedding phase (c) post processing phase as discussed below.

      1. Pre processing phase

        Step 1 Let C and S represents colored host or Original image and secure image respectively. Dimension of both images is same i.e. 256*256. The Original image is split into three primary color components red (R), green (G), blue (B). Similarly, secure image also undergoes the same process.

        Secure Image

        Embedded Image

        Fig. 5. Proposed embedding scheme block diagram.

        Step 2 Apply three level redundant discrete wavelet transform to decompose the approximate or LL band of R, G, B primary components of both Original as well as secure image as shown in the fig. 2.

      2. Embedding phase

      Step 3 After performing 3 level decomposition of LL band LL3, LH3, HL3, HH3 sub bands are obtained. Out of these sub bands LH3 sub band of all the basic color components is selected and SVD is computed to obtain ULH3, SLH3, VLH3 matrices. As already explained above, LH band also known as horizontal band of an image is a low frequency band so a little alteration in this sub band will not show much effect in the watermarked image and hence imperceptibility is maintained throughout the numerical computation.

      (6)

      Step 4 After obtaining the singular value SLH3 for both host and watermark image, select a fixed scaling or strength factor () for modifying the singular value of host image. This factor enhances the security of used technique and makes it cumbersome task for any illegitimate user to tamper the digital data. Following computation is used:

      (7)

      Step 5 This altered singular value obtained from above equation (MSLH3_C) replaces the original singular value (SLH3_C) in the Original image. This replacement is done for all the primary colors (R, G, B) into which the colored digital image is split.

      (8)

      Embedded Image

      Step 6 After computing SVD, singular value of watermarked image is obtained. New LH3 sub band of watermarked image is as follows:

      (9)

      1. Post processing phase

      Step 7 This uncovered or split image is now wrapped back by performing 3 levels Inverse redundant discrete wavelet transform (IRDWT). All the color components are merged together; this completes the embedding process thereby creating a watermarked image.

    2. Extraction scheme

    Extraction is just the reverse of embedding process as can be seen in figure 6. Extraction of watermark image is done in order to make sure about the ruggedness, imperceptibility and authenticity of lawful user. Extraction process is also divided into three steps: (a) pre-processing phase (b) extraction phase

    (c) post processing phase as discussed below.

    1. Pre processing phase

      Step 1. Watermarked image is further split into its primary color components (R, G, B) as done earlier for Original image. This image is either embedded image or it can be distorted image which is affected by various attacks and noises.

      Step 2 Three level RDWT is applied in the LL sub band of watermarked image. RDWT is applied on all the color components separately. This step decomposes the LL sub band from LL1 level to LL3 level.

      Secure Image

      Fig. 6.Proposed extraction scheme block diagram.

    2. Extraction phase

      Step 3 Select LH3 sub band and apply SVD for obtaining the singular value of secure image. Singular value of secure image (SLH3_EWM) is extracted using equation below:

  4. EVALUATION PARAMETER

    Another quantity used to qualitatively measure the proposed works robustness is mean square error (MSE), which should be as least as possible. MSE is represented as follows

    It is obtained for all the three color components.

    (10)

    (12)

    Step 4 LH3 sub band of extracted watermark image ( ) is multiplication of secure image U ) and V ( ) matrices with the Singular value of extracted watermarked image ).

    (11)

    1. Post processing phase

      Step 5 All the layers of extracted watermark are again wrapped up using 3 levels IRDWT. The three color components are merged together and this completes the extraction of embedded secure image.

      To measure the rigidity and soundness of the scheme PSNR is computed. PSNR is the ratio of peak signal power to peak noise power and in terms of image processing it is used to represent how much invisible is the secure image in watermarked image. PSNR should be as large as possible. Mathematically, it can be represented as

      (13)

      The mathematical value that shows the resemblance between secure image and extracted secure image is non correlation (NC). It is another important parameter for analyzing the imperceptibility of the scheme and is represented as

      (14)

      1. (b) (c)

        Fig.7. The watermarked images (a) Lena, (b) Mandrill, (c) Peppers and extracted watermark color chips.

        1. (b) (c)

    Fig. 8.The watermarked images (a) Lena, (b) Mandrill, (c) Peppers and extracted watermark splash.

    (a) (b) (c)

    Fig. 9.The watermarked images (a) Lena, (b) Mandrill, (c) Peppers and extracted watermark plane.

    (a) (b) (c)

    Fig. 10.The watermarked images (a) Lena, (b) Mandrill, (c) Peppers and extracted watermark 8color image.

    (a) (b) (c)

    (d)

    Fig. 11.The watermarked image (Lena) and extracted watermark (Color chips) after applying additive noise attack (a) Salt & pepper noise, (b) AWGN, (c) Speckle noise and (d) Poisson noise.

    1. (b) (c)

      Fig.12. The watermarked image (Lena) and extracted watermark (Color chips) after applying filtering attack (a) Gaussian Filter, (b) Median Filter, (c) Average Filter.

      1. (b)

    Fig.13. The watermarked image (Lena) and extracted watermark (Color chips) after applying geometrical attack (a) Shifting, (b) Rotation.

    1. (b) (c)

      Fig.14. The watermarked image (Lena) and extracted watermark (Color chips) after applying various attacks (a) Contrast, (b) Gamma Correction, (c) Histogram Equalization.

      1. (b) (c)

    (d)

    Fig.16. The watermarked image (Lena) and extracted watermark (Splash) after applying additive noise attack (a) Salt & pepper noise, (b) AWGN, (c) Speckle noise and (d) Poisson noise.

    1. (b) (c)

      Fig. 17. The watermarked image (Lena) and extracted watermark (Splash) after applying filtering attack (a) Gaussian Filter, (b) Median Filter, (c) Average Filter.

      1. (b)

    Fig.18. The watermarked image (Lena) and extracted watermark (Splash) after applying geometrical attacks (a) Shifting, (b) Rotation.

    (a) (b) (c)

    Fig 19. The watermarked image (Lena) and extracted watermark (Splash) after applying different attacks (a) Contrast, (b) Gamma Correction, (c) Histogram Equalization.

    TABLE I. PSNR, MSE, NC RESULT AFTER EMBEDDING

    Host Image

    Watermark Image

    PSNR

    MSE

    NC

    Lena

    Color chips

    70.0219

    0.00647

    0.999749

    Air plane

    66.3495

    0.015071

    0.999774

    Splash

    69.4055

    0.007456

    0.999608

    8 Color

    68.227

    0.009781

    0.99987

    Mandrill

    Color chips

    73.171

    0.003133

    0.999746

    Air plane

    67.8716

    0.010615

    0.999776

    Splash

    70.7689

    0.005447

    0.999605

    8 Color

    68.3507

    0.00956

    0.99987

    Pepper

    Color chips

    72.2359

    0.003886

    0.999746

    Air plane

    67.2738

    0.012182

    0.999769

    Splash

    71.3217

    0.004796

    0.999597

    8 Color

    69.2598

    0.007711

    0.999871

  5. EXPERIMENTAL RESULTS AND DISCUSSION

    Research work is carried out for colored host and secure images having same dimensions i.e. 256*256. MATLAB 2012a is used as software tool for carrying out watermarking experiments. Three host images Lena, Mandrill and Pepper whereas four secure images color chips, splash, plane, and 8 colors are used in the research work. Database CVG-UGR

    [28] is used for selection of host and secure images as can be seen in fig 3 and fig 4. In the proposed work performance of blind color watermarking scheme is analyzed and studied on the basis of imperceptibility and robustness.

    1. Imperceptibility results

      As already discussed above that imperceptibility is a parameter used in image processing that exhibits the quality of watermarking scheme. Calculation of PSNR depicts the invisibility of secure image in watermarked image. Generally, it is considered that Human Visual System (HVS) can not distinguish between original image and watermarked image if

      value of PSNR is above 30 dB. As the value of PSNR increases beyond the prescribed limit it makes the experimental results more and more imperceptible. Not only PSNR but Mean Square Error (MSE) is also one of the mathematical tools used in image processing employed for determining the quality of image. MSE represents accumulative squared error between the original image and watermarked image. The value of MSE should be as low as possible for it can be seen from table 1 that the proposed watermarking scheme has achieved favorable PSNR, MSE and NC results. Obtained PSNR value is even greater than 70 dB and MSE value is too much smaller for e.g. when host image is Pepper and secure image is color chips then PSNR is 72.2359 and MSE is 0.0038859

      In table 2 the defined scheme is compared with various other watermarking schemes [1,2,3,4,5,6,7,8,9] and it is observed that the research work done in this paper is giving best output at present. From table 4 it can be noticed that the colored host images after undergoing through various attacks are extracted with a good PSNR value.

      TABLE II. COMPARISION OF PSNR AND NC WITH OTHER TECHNIQUES

      Host Image

      Sharma et al. [1]

      Su et al. [2]2019

      Su et al. [3]2017

      Ansari et al. [4]

      Su et al. [5]-35

      Patwardh an et al. [6]

      Kalra et al. [7]

      Ali et al. [8]

      Abdelhaki m et al. [9]

      Proposed scheme

      Lena

      60.1620 /

      0.9998

      37.9574 /

      0.9409

      49.9898 / *

      45.1242 / *

      39.448 / 0.9816

      54.9980 /

      0.9909

      42.0100 / *

      44.0207 / *

      53.9400 / *

      68.2270 /

      0.99987

      Pepper

      67.4618 /

      0.9995

      37.8108 /

      0.9274

      50.0839 / *

      44.9243 / *

      40.8216 /

      0.9878

      *

      42.6800 / *

      43.0222 / *

      54.6000 / *

      69.2598 /

      0.999871

      Baboon

      *

      37.8179 /

      0.9787

      49.8901 / *

      *

      *

      55.1586 /

      0.9799

      36.1100 / *

      40.0256 / *

      48.0900 / *

      6803507 /

      0.99987

    2. Robustness results

    Robustness is another important parameter in image processing that mathematically determines the quality of extracted secure image after going through different attacks. Non correlation (NC) is the mathematical function used for analyzing the robustness of watermarking scheme and shows the resemblance between extracted secure image and original secure image. Generally the value of NC lies between 0 and

    1. The closer the value of NC with 1, more robust is the proposed scheme.

    In table 5 NC value is determined for different host images under various interferences. It can be noticed from table that the designed scheme has NC values lying nearer to

    1. Table 3 shows the comparison of NC values with other schemes [1, 4, 8, 10] and it is found out that the proposed research work has NC values closer to 1 and better than the watermarking research done so far. This shows the rigidity and robustness of watermarking scheme carried out in this paper.

      TABLE III. COMPARISION OF NC VALUE WITH OTHER TECHNIQUES

      Attacks

      Parameter

      Proposed Scheme

      Sharma et al. [1]

      Vali et al. [10]

      Ansari et al. [4]

      Ali et al. [8]

      M = 0; V = 0.001

      0.999518

      0.9965

      0.9838

      0.983

      AWGN

      M = 0; V = 0.01

      0.996135

      0.9914

      0.9304

      M = 0; V = 0.1

      0.97526

      0.9812

      0.9179

      v = 0.02

      0.998183

      Speckle Noise

      v = 0.001

      0.999677

      0.9964

      0.9953

      v = 0.01

      0.99912

      0.9899

      0.9666

      v = 0.1

      0.990588

      0.9813

      0.921

      d = 0.05

      0.993611

      Salt and pepper Noise

      d = 0.001

      0.999686

      0.9965

      0.9962

      0.9989

      d = 0.01

      0.998964

      0.9916

      0.9688

      0.8904

      d = 0.1

      0.987412

      0.9832

      0.8924

      Contrast Attack

      0.994815

      0.9797

      Poisson Noise

      d = 0.05

      0.999356

      Shift Attack

      0.999749

      Rotation Attack

      Angle = 5

      0.999749

      0.9914

      Angle = 2

      0.999749

      0.9947

      0.9921

      Histogram

      0.989632

      0.9725

      0.9721

      0.9878

      0.9982

      Equalization

      Gaussian Filter

      [3 3]

      0.998912

      0.9959

      0.9832

      [5,5]

      0.99764

      0.9958

      0.9899

      Median Filter

      [3,3]

      0.999434

      0.9955

      0.9716

      0.9896

      0.9076

      Gamma Correction

      0.3

      0.997636

      0.8

      0.999648

      0.989

      0.9973

      0.9949

      0.9663

      Average Filter

      [3 3]

      0.998817

      0.9948

      0.9496

      0.9751

      TABLE IV. PSNR VALUE OF VARIOUS WATERMARKED AND EXTRACTED WATERMARK IMAGE UNDER VARIOUS ATTACKS

      Host Image

      Lena

      Mandrill

      Pepper

      Multiple Attacks

      Color Chips

      Air Plane

      Splash

      8 Color

      Color Chips

      Air Plane

      Splash

      8 Color

      Color Chips

      Air Plane

      Splash

      8 Color

      PSNR_WM

      70.0219

      66.3495

      69.4055

      68.227

      73.171

      67.8716

      70.7689

      68.3507

      72.2359

      67.2738

      71.3217

      69.2598

      PSNR_E

      50.0739

      45.5653

      48.5875

      47.4894

      50.0779

      45.5641

      48.5845

      47.4889

      50.0759

      45.5663

      48.5821

      47.4894

      AWGN(m = 0; v = 0.001)

      47.0145

      44.17

      46.169

      45.861

      48.5624

      45.0085

      47.4049

      46.7467

      47.212

      44.4092

      46.465

      46.0477

      Speckle(v = 0.02)

      38.0948

      37.4423

      37.8675

      38.7927

      41.1426

      40.1268

      41.0283

      41.3878

      39.1716

      38.4245

      38.9836

      39.8285

      Salt & Pepper (d = 0.05)

      32.533

      32.2479

      32.4201

      33.6814

      35.0191

      34.4838

      34.8898

      35.9467

      32.8614

      32.6965

      32.7334

      35.8375

      Contrast

      35.9164

      35.5355

      35.8997

      37.154

      28.6262

      28.4021

      28.6631

      30.1236

      31.8161

      31.5038

      31.7815

      30.1236

      Poisson(d = 0.05)

      44.0655

      42.4801

      43.6476

      43.954

      46.7602

      43.9659

      45.8213

      45.6396

      45.1632

      43.0954

      44.579

      45.584

      Shift

      50.074

      45.5652

      48.5875

      47.4894

      50.0779

      45.5641

      48.5845

      47.4889

      50.0759

      45.5663

      48.5821

      47.4889

      Rotation(angle=2)

      50.0739

      45.5653

      48.5875

      47.4894

      50.0779

      45.5641

      48.5845

      47.4889

      50.0759

      45.5663

      48.5821

      47.4894

      Histogram

      30.5008

      30.2645

      30.4142

      31.8769

      27.4614

      27.2383

      27.395

      29.0183

      27.5218

      27.421

      27.5348

      29.2219

      Gaussian filter(5*5)

      39.1499

      38.5201

      38.981

      39.7613

      33.77

      33.483

      33.6759

      34.8384

      37.7196

      37.2531

      37.6035

      38.4978

      Median filter(3*#)

      46.9781

      44.2491

      46.1635

      45.8289

      39.2363

      38.5817

      39.0721

      39.8428

      46.6134

      44.0268

      45.8007

      45.5865

      Gamma correction(0.8)

      48.3407

      44.9032

      47.2727

      46.7194

      47.8944

      44.7214

      46.9626

      46.4778

      48.9212

      45.2164

      47.8238

      47.0362

      Average filter(3*3)

      42.2326

      41.0185

      41.9247

      42.6157

      37.2083

      36.745

      37.086

      38.0327

      41.2102

      40.2232

      40.9913

      41.704

      TABLE V. NC VALUE OF SECURE IMAGE (COLOR CHIPS, AIRPLANE, SPLASH) UNDER VARIOUS ATTACKS

      Watermark

      Color chips

      Airplane

      Splash

      8 color

      Attacks

      Parameter

      Lena

      Mandrill

      Lena

      Mandrill

      Pepper

      Pepper

      Lena

      Mandrill

      Pepper

      Lena

      Mandrill

      Pepper

      M = 0; V = 0.001

      0.999518

      0.999619

      0.999868

      0.999872

      0.999871

      0.999565

      0.999734

      0.999734

      0.999717

      0.999007

      0.999298

      0.999117

      AWGN

      M = 0; V = 0.01

      0.996135

      0.997205

      0.999712

      0.999763

      0.999742

      0.996779

      0.999462

      0.999565

      0.999479

      0.99078

      0.992993

      0.99231

      M = 0; V = 0.1

      0.97526

      0.976226

      0.999265

      0.999401

      1.00044

      0.978348

      0.994448

      0.99504

      0.995871

      0.951211

      0.953599

      0.954162

      v = 0.02

      0.998183

      0.998666

      0.999802

      0.999813

      0.999804

      0.998046

      0.999617

      0.999669

      0.999578

      0.995584

      0.996773

      0.995262

      Speckle Noise

      v = 0.001

      0.999677

      0.99972

      0.999871

      0.999874

      0.999872

      0.999693

      0.999765

      0.999767

      0.999775

      0.99947

      0.999605

      0.999507

      v = 0.01

      0.99912

      0.999359

      0.99984

      0.999854

      0.999836

      0.99907

      0.999684

      0.999359

      0.999698

      0.997868

      0.998486

      0.997782

      v = 0.1

      0.990588

      0.990686

      0.999597

      0.999688

      0.999556

      0.990061

      0.998386

      0.998444

      0.998517

      0.977487

      0.977809

      0.975331

      d = 0.05

      0.993611

      0.996024

      0.99962

      0.999686

      0.999613

      0.99423

      0.999078

      0.999377

      0.999222

      0.984619

      0.990078

      0.985502

      Salt and pepper Noise

      d = 0.001

      0.999686

      0.999724

      0.99987

      0.999868

      0.999869

      0.9997

      0.999767

      0.999762

      0.999771

      0.99953

      0.999572

      0.999524

      d = 0.01

      0.998964

      0.999394

      0.99983

      0.999849

      0.999841

      0.999006

      0.999688

      0.999705

      0.99969

      0.997465

      0.998612

      0.997801

      d = 0.1

      0.987412

      0.990847

      0.999649

      0.999684

      0.999519

      0.988144

      0.998033

      0.998478

      0.99827

      0.970559

      0.978287

      0.972063

      Contrast Attack

      0.994815

      0.983238

      0.999488

      0.998956

      0.999093

      0.991071

      0.998934

      0.995864

      0.997505

      0.98686

      0.967945

      0.979826

      Poisson Noise

      d = 0.05;

      0.999356

      0.99947

      0.999855

      0.999863

      0.999858

      0.999365

      0.99971

      0.999739

      0.999685

      0.998557

      0.998878

      0.998576

      Shift Attack

      0.999749

      0.999746

      0.99987

      0.99987

      0.999871

      0.999746

      0.999774

      0.999776

      0.999769

      0.999608

      0.999605

      0.999597

      Rotation Attack

      Angle = 5

      0.999749

      0.999746

      0.99987

      0.99987

      0.999871

      0.999746

      0.999774

      0.999776

      0.999769

      0.999608

      0.999605

      0.999597

      Angle = 2

      0.999749

      0.999746

      0.99987

      0.99987

      0.999871

      0.999746

      0.999774

      0.999776

      0.999769

      0.999608

      0.999605

      0.999597

      Histogram Equalization

      0.989632

      0.985711

      0.999086

      0.998704

      0.998463

      0.983022

      0.99768

      0.996606

      0.994643

      0.979091

      0.972744

      0.972538

      Gaussian Filter

      [3 3]

      0.998912

      0.997177

      0.999807

      0.999757

      0.9998

      0.998578

      0.999628

      0.999421

      0.999575

      0.997511

      0.993205

      0.996593

      [5 5]

      0.99764

      0.994155

      0.999762

      0.999686

      0.99974

      0.996569

      0.999481

      0.999064

      0.999347

      0.994349

      0.986557

      0.991941

      Median Filter

      0.999434

      0.997865

      0.999842

      0.999775

      0.999842

      0.999428

      0.999705

      0.999519

      0.999713

      0.998871

      0.994955

      0.999597

      Gamma Correction

      0.3

      0.997636

      0.995208

      0.99982

      0.999681

      0.999913

      0.998483

      0.999467

      0.999052

      0.99959

      0.994081

      0.988695

      0.996366

      0.8

      0.999648

      0.999569

      0.999864

      0.999857

      0.99987

      0.999658

      0.999753

      0.999722

      0.999752

      0.999401

      0.999146

      0.999435

      Average Filter

      [3 3]

      0.998817

      0.996876

      0.999815

      0.999739

      0.999806

      0.998423

      0.999614

      0.999397

      0.999586

      0.997316

      0.992477

      0.996221

      Fig. 20.Graphical comparison of PSNR of various techniques and proposed technique.

      1. (b)

    (c)

    Fig. 21.Graphical results of (a) PSNR, (b) MSE, and (c) NC of different host and watermark images of proposed scheme.

  6. CONCLUSION

A rigid, sound and unobtrusive blind color watermarking scheme is discussed in this research paper. Colored host and secure images of same dimension are used; this increases the embedding capacity of secure image and thus making it cumbersome task for unauthorized user to extract the secure image thereby enhancing the security feature of proposed scheme. This paper explored the intelligence of

multi level RDWT and SVD techniques in order to obtain acceptable and satisfactory results of PSNR, MSE and NC. Not only multi level RDWT and SVD is employed but also embedding strength factor is used during embedding in order to further increase the security thereby increasing efficiency of proposed research. To prove the quality and rigidity of the work done the watermarked image is made to pass through various attacks like noises (AWGN,

speckle noise, salt and pepper noise, Poisson noise), geometrical attacks (rotation, shifting), filtering attacks (median, Gaussian, average filtering) and also through contrast, histogram equalization, gamma correction. Above mentioned noises are used with different parameters to examine the caliber and worth of mechanism used for processing color image watermarking. After the application of above mentioned techniques and constraints, it is concluded that the results obtained are up to the mark and satisfactory. These results are also compared with other schemes and are best of our knowledge at present.

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