Use of DWT: Replacement Phenomenon of Digital Image Watermarking

International Journal of Emerging Technology and Advanced Engineering

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Use of DWT: Replacement Phenomenon of Digital Image

Watermarking

Dr. Uttam D. Kolekar

_{, Amol R. Madane}

1_{Research Guide and Principal, Parshwanath College of Engineering, Thane (W), Mumbai- 400615, India} 2_{Research Scholar, JJT University, Jhunjhunu, Rajasthan- 333001, India}

1_{[email protected]} 2_{[email protected]}

Abstract— The manuscript presents the replacement

algorithm of digital image watermarking. The tool used for processing is discrete wavelet transform (DWT). The method includes the process of dividing the digital image into multiple frequency bands which are replaced by the content of secrete image. This phenomenon is used to provide copyright protection, imperceptibility to the user. Peak signal to noise ratio (PSNR), mean square error (MSE), correlation coefficients (CC) are the parameters used to check the effectiveness of algorithm. This presented phenomenon is tested with the help of different set of host images and secretes information.

Keywords— Correlation Coefficients, Discrete Wavelet

Transform, Inverse Discrete Wavelet Transform, Mean Square Error, Peak Signal to Noise Ratio, Replacement Algorithm.

I. INTRODUCTION

Digital image watermarking is set of methods, processes and technologies that add the secrete information in the host image. The secrete information may be number or text or image, in digital media such as images, video or audio. The embedding takes place by manipulating the content of digital data. The information is not embedded in the places. The information hiding process has to be such that the modification of the media is imperceptible. For images this means that the modifications of the pixel values have to be invisible. Furthermore, the watermark must be either robust or fragile, depending on the application. By "robust" we mean the capability of the watermark to resist manipulations of the media, such as lossy compression (where compressing data and then decompressing it retrieves data that may well be different from the original, but is close enough to be useful in some way), scaling, and cropping, just to enumerate some. In some cases the watermark may need to be fragile. "Fragile" means that the watermark should not resist tampering, or would resist only up to a certain, predetermined extent.

In this paper, we will discuss the replacement phenomenon of digital image watermarking. This phenomenon is based on different wavelet components. Section 2 reviews the literature survey on digital image watermarking using Discrete Wavelet Transform. Section 3 defines the problem for which we need to find the solution. Section 4 discusses the proposed algorithm. Section 5 will give an idea about the results and summary of discussion.

II. LITERATURE SURVEY

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Authors [7] proposed the watermarking algorithm by using the tool like discrete wavelet transform and discrete cosine transform. Z. Yucel, A.B. Ozguler [8] explained new method for digital image watermarking based on zero assigned filter banks and embedded zero tree wavelet (EZW) algorithms. Authors [9] highlighted the review of recent as well as classic image copy detection method.

III. PROBLEM DEFINITION

In case of digital image watermarking, the secrete data such as digital image is inserted into the host image for content identification and management, content protection for audio and video content, Forensics and piracy deterrence Content filtering (includes blocking and triggering of actions) Communication of ownership and copyrights. In the previous algorithms, authors have inserted the secrete information with the help of different tools such as human visual system (HVS), 2D datamatrix etc. The proposed algorithm presents the new approach to insert the secrete information into the host image. This adds more value to authentication and security.

IV. PROPOSED ALGORITHM

A. Replacement Algorithm

a) Consider any gray scale image.

b) Decompose the input image by using 2D discrete wavelet transform (DWT). Store the first level approximation coefficients i.e. ILL1, horizontal coefficient ILH1, vertical coefficient IHL1, diagonal coefficient IHH1 as first level watermark key coefficients of input image.

c) Approximation coefficient of first level of input image is ILL1 which is further decomposed into new coefficients i.e. ILL2, horizontal coefficient ILH2, vertical coefficient IHL2, diagonal coefficient IHH2 as second level watermark key coefficients of input image using 2D discrete wavelet transform.

d) Approximation coefficient of second level of input image is ILL2 which is further decomposed into new coefficients i.e. ILL3, horizontal coefficient ILH3, vertical coefficient IHL3, diagonal coefficient IHH3 as third level watermark key coefficients of input image using 2D discrete wavelet transform.

e) Consider the gray scale image.

f) Decompose the secrete information by using 2D discrete wavelet transform. Store the first level approximation coefficients i.e. SLL1, horizontal coefficient SLH1, vertical coefficient SHL1, diagonal

coefficient SHH1 as first level watermark key coefficients of secrete information.

g) Approximation coefficient of first level of secrete information is SLL1 which is further decomposed into new coefficients i.e. SLL2, horizontal coefficient SLH2, vertical coefficient SHL2, diagonal coefficient SHH2 as second level watermark key coefficients of secrete information using 2D discrete wavelet transform.

h) Reduce the intensity value of SLL2 part of secrete information to half i.e. SLL2mod. This is used to insert the value of pixels in IHH3 of input image.

i) Replace (insert) the IHH3 part of input image by SLL2mod part of secrete information pixel by pixel. j) Perform the three level 2D inverse discrete wavelet

transform (IDWT) of input image by using approximation coefficients of input image to find the Invisible Watermark Image using replacement algorithm. Gray scale invisible watermark image is obtained, which is perceptually equal to original input image.

k) Find the mean square error (MSE), peak signal to noise ratio (PSNR) and correlation coefficients (CC) in between the original input image and invisible watermark image by using the related formulae as these are the important performance parameters. l) We can make program generalize as per the size is

concern i.e. we can change the size of input image and secrete information by changing the size parameters in program. But only essential condition is that both images should be square images.

B. Extraction Algorithm

m) Consider invisible watermark image as input image. n) Decompose the invisible watermark image by using

2D discrete wavelet transform. Then we will get the first level approximation coefficients i.e. InverseLL1, horizontal coefficient InverseLH1, vertical coefficient InverseHL1, diagonal coefficient InverseHH1 as first level watermark key coefficients of invisible watermark image.

o) Approximation coefficient of first level is InverseLL1 which is further decomposed into new coefficients i.e. InverseLL2, horizontal coefficient InverseLH2, vertical coefficient InverseHL2, diagonal coefficient InverseHH2 as second level watermark key coefficients of invisible watermark image.

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coefficient InverseHH3 as third level watermark key coefficients of invisible watermark image.

q) Consider the InverseHH3 part of invisible watermark image as secrete information and multiply by the scaling factor to get NewSLL2.

r) Perform the two level 2D inverse discrete wavelet transform (IDWT) of secrete information by using approximation coefficients of secrete information to find the secrete information using replacement algorithm. Gray scale secrete information is obtained, which is perceptually equal to secrete information. s) Find the mean square error (MSE), peak signal to noise

ratio (PSNR) and correlation coefficient (CC) in between the original secrete information and extracted secrete information by using the related formulae as these are the important performance parameters.

V. PERFORMANCE PARAMETERS

The performance parameters used to estimate the performance of developed replacement algorithm are peak signal to noise ratio (PSNR), mean square error (MSE), correlation coefficient (CC).

VI. RESULT ANALYSIS

The secrete information used for estimation purpose is as shown in figure 1.

FIG 1:SECRETE INFORMATION (256X256)

The different input images used for experimentation purpose are shown in figure 2.

(A) (B)

(C) (D)

(E) (F)

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(I) (J)

(K) (L)

(M) (N)

FIG 2:DIFFERENT INPUT IMAGE (512X512)(A)AMOL,(B)BABY,(C) BOAT,(D)BRAIN,(E)BRIDGE,(F)CROWD,(G)FINGER,(H)LAX,(I)LENA,

(J)PANCARD,(K)PEPPERS,(L)TEXT,(M)WOMAN1,(N)WOMAN2

TABLE 1:PERFORMANCE PARAMETERS FOR ORIGINAL IMAGE AND INVISIBLE WATERMARK IMAGE (AFTER APPLYING REPLACEMENT

PHENOMENON)

Input Image Original Image and Watermark Image

PSNR MSE CC

Amol 78.2167 9.7275e-004 0.9972 Baby 78.2508 9.7275e-004 0.9753 Boat 78.0090 9.7275e-004 0.9924 Brain 78.2508 9.7275e-004 0.9909 Bridge 78.2508 9.7275e-004 0.9900 Crowd 78.2508 9.7275e-004 0.9877 Finger 78.2508 9.7275e-004 0.9742

Lax 77.6880 9.7275e-004 0.9785 Lena 78.2508 9.7275e-004 0.9901 Pancard 78.2508 9.7275e-004 0.9873 Peppers 77.2405 9.7275e-004 0.9920 Text 78.2167 9.7275e-004 0.9919 Woman1 76.1839 9.7275e-004 0.9868 Woman2 78.2167 9.7275e-004 0.9950

TABLE 2:PERFORMANCE PARAMETERS FOR ORIGINAL SECRETE INFORMATION AND THE EXTRACTED SECRETE INFORMATION

Input Image Original Image and Watermark Image

PSNR MSE CC

Amol 72.2302 0.0039 0. 8627 Baby 72.2302 0.0039 0.9860 Boat 72.2302 0.0039 0.9965 Brain 72.2302 0.0039 0.9846 Bridge 72.2302 0.0039 0.9947 Crowd 72.2302 0.0039 0.9862 Finger 72.2302 0.0039 0.9965 Lax 72.2302 0.0039 0.9967 Lena 72.2302 0.0039 0. 9979 Pancard 72.2302 0.0039 0. 9886 Peppers 72.2302 0.0039 0.9964 Text 72.2302 0.0039 0.6856 Woman1 72.2302 0.0039 0.9903 Woman2 72.2302 0.0039 0.9948

While using the replacement phenomenon, the secrete information is replaced in host to get the watermark image. The performance comparison of host image and watermark image are shown in table 1. The evaluation of performance between secrete information and extracted secrete information is as captured in table 2.

If we observe the values of performance parameters for watermark images (table 1) and secrete information (table 2), Lena image gives best results in all the respects. Hence, we selected the same image for future research work.

VII. CONCLUSION

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REFERENCES

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[3]. A. Essaouabi, E. Ibnelhaj, F. Regragui, “Digital Image Watermarking for Arbitrarily Shaped Objects Based on SA-DWT”, International Journal of Computer Science Issues, Vol. 5, No. 1, Oct 2009, pp 1-8.

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[5]. Shikha Tripathi, Nishanth Ramesh, Bernito, K J Neeraj, “A DWT based Dual Image Watermarking Technique for Authenticity and Watermark Protection”, International Journal of Signal and Image Processing, Vol. 1, No. 2, Dec 2010, pp. 33-45.

[6]. Akhil Pratap Singh, Agya Mishra, “Wavelet Based Watermarking on Digital Image”, Indian Journal of Computer Science and Engineering, Vol. 1, No. 2, Aug-Sep 2010, pp. 86-91.

[7]. Dr. Bassim Abdulbaki Jumma, Arwa Aladdin, “Image Watermarking using DWT_DCT”, Engineering and Technology Journals, Vol. 28, No. 23, Nov 2010, pp. 1-14,

[8]. Yucel Z., Ozguler A.B., “An image watermarking algorithm via zero assigned filter banks”, Proceedings of Fifth IEEE International Symposium on Signal Processing and Information Technology, Dec 2005, pp. 363-368,.