DCT-SVD And DWT-SVD Image Watermarking ...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 9, Number 24 (2014) pp. 25833-25839 © Research India Publications http://www.ripublication.com

DCT-SVD And DWT-SVD Image Watermarking Techniques 1

Raji Pandurangan and 2Dr.E.Logashanmugam

Research Scholar(IEEE student Member),Assistant Professor, Bharath University, [email protected] Head/Faculty of Electrical and Electronics, Sathyabhama University,Chennai,[email protected]

Abstract: As Advancing techniques are increasing in data communication, the problem of handling the intruder is becoming tedious. Image watermarking provides rigid security by authentication and copyright protection. There are different imagewatermarking techniques in Spatial and frequency domains. In this paper we are performing SVD transform along with frequency domain digital image watermarking methods like DCT and DWT for invisible watermarking. Comparison of thesame is done in terms of robustness and transparency to have a better idea of correlation of SVD. Keywords: Image Watermarking, DWT, DCT, SVD

I. INTRODUCTION Watermarking is the popular approach for information protection in the fields of data processing, signal processing, speech processing, imageprocessing, videoprocessing. Watermarking is a technique that provides copyright protection and authentication of the original data by incorporating the watermark (logo, symbols, brand names, text, and image) into the cover image. Different techniques are available for performing watermarking using various image transformation methods. The techniques are mainly classified as spatial and frequency domain. Under spatial domain we have LSB, Bit plane based on pixel modifications. Later method constitutes of embedding of information in frequency coefficients. Namely DFT, DCT, DWT are commonly used. Discrete Cosine Transform generates elementary frequency components of given image .It defines an image in terms of cosine function of The discrete cosine transforms is a technique for converting a signal into elementarymagnitudes and

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frequencies.[9].Embedding of watermark is done by modifying the coefficients of the middle frequency sub-band hence the appearance of image is not affected and also cannot be removed by compression. Wavelet transform can store information in the detail components known as sub bands LL,LH,HL,HH Watermark is embedded using DWT in any of the sub bands, it decomposes the image of size M×N using Dydic Filters to NLevels,Extraction of Watermark can be done using Inverse Discrete Wavelet Transform (IDWT). In Singular Value Decomposition a matrix is decomposed into orthogonal matrices of singular values(eigen values).When considering an image it divides an image into 3 eigen images. SVD decomposes a matrix into orthogonal components with which optimal sub rank approximations may be obtained The largest object components in an image found using the SVD generally correspond to eigenimages associated with the largest singular values, while image noise corresponds to eigenimages associated with the smallest singular values. The SVD is used to approximate the matrix decomposing the data into an optimal estimate of the signal and the noise components. This property is one of the most important properties of the SVD decomposition in noise filtering, compression and forensic which could also treated as adding noise in a proper detectable way.

II. ALGORITHMS Discrete cosine Transform: It helps to separate the image into parts or spectral sub bands .It transforms a signal or image from spatial domain to frequency domain .i.e f(i,j) to F(u,v). DCT encoding for 1D is ∑ () (2 + 1) ( ) and IDCT F-1(u) as F(u,v)=√ IDDCTf(u,v) = 1/√2 1 ℎ

=0

f(i,j) is intensity of pixel in row i ,column j and F(u,v) is DCT Coefficients in row k1 and k2 of DCT matrix. Most of images signal energy lies at low frequency and appear in upper left corner of DCT. Compression is possible at lower right high frequency as small details missed results in it does not have effect in output image. For 8 point 8×8 has (0-255) and output array of DCT range from -1024 to 1023. DCT requires less addition and multiplications and shifts .World record is 11 multiplications and 29 additions.(C.Loeffer, A.Lightenber G and G.Moschytz, “Practical Fast 1D DCT Algorithm with Multiplication,Proc int’l Conf. on Acoustic speech and Signal Processing 1989 ICASSP’89)”[7].DCT is cut down of FFT only the real terms are considered. Discrete Wavelet Transformation: DWT are expansion functions are “small areas of finite Durations and varying frequency .When digital images are to be viewed or processed at multiple resolutions,DWT is best. DWT encompasses a variety of unique but related

DCT-SVD And DWT-SVD Image Watermarking Techniques

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transformations cannot be written in single equation to describe them all .The various transformations are related by the factor that their expansions functions are small waves hence name wavelets of varying frequency and limited duration. Singular value decomposition: SVD is a mathematical algorithm to analyze matrices for a variety of applications. Matrix ‘M’ is decomposed into three sub matrices [u, s, v] such that: M=[u][s][vT]Where ‘u’ and ‘v’ are the orthogonal matrices such that uuT = I and vvT = I where ‘I’ is the Identity matrix and ‘s’ is the diagonal matrix× (s1, s2, s3 ………sN) such that s1>=s2>=s3……..s(N-1)>=sN[2]. Matrix s values are known as singular values, and matrices u and v are known as corresponding singular vectors. When SVD is applied to the image we get singular values (diagonal matrix’s’) that represent the luminance or color intensity of the image and the matrices ‘u’ and ‘v’ represents the geometry of the image. It is proved that SVD provides better visual perception along with the robustness without changing its perception.Singular values exhibit some more properties like rotation invariance, translation invariance, transposition invariance, etc. These all properties of SVD are much desirable in watermarking. DCT-SVD: A combination of DCT and SVD is done to improve robustness in algorithms by utilizing the properties of both transforms. In this approach firstly, in embedding process the DCT is applied to the cover image. Using the zigzag sequence, map the DCT coefficients into 4 quadrants. Then SVD is applied to each quadrant. Similarly DCT coefficients of watermark will be determined and will be added to the cover image DCT coefficients and obtain the 4 sets of modified DCT coefficients. Then map the modified DCT coefficients back to their original positions and the inverse DCT is applied to produce the watermarked cover image. Secondly in the extraction process, the DCT is applied to the visible watermarked image. Then using the zigzag sequence, map the DCT coefficients into 4 quadrants and extract the singular values from each quadrant. Finally apply the inverse DCT to each set to construct the four visual watermarks. DWT-SVD: In embedding apply DWT to decompose the cover host image into four nonoverlapping multi-resolution sub-bands: LL1, HL1, LH1, and HH1. Apply DWT to the HL1 sub-band to get four smaller sub-bands, and choose the sub-band HL2 Or, apply DWT to the HH1 sub-band to get four smaller sub-bands and choose the subband HH2, Divide the sub-band HL2 (or HH2) into 4x4 blocks. Apply SVD to each block in HL2 (or HH2). Re-formulate the grey-scale watermark image into a vector of zeros and ones. Modify the singular values matrix s of each block according to the value of the watermark bit. If the watermark bit is 0, S is modified according to the watermark embedding formula otherwise, if the watermark bit is 1, S remains unchanged. Apply the inverse DWT (IDWT) on the DWT transformed image, including the modified sub-band, to produce the watermarked cover host image.

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In extraction procedure decompose the watermarked image using DWT into four non-overlapping multi-resolution sub-bands: LL1, HL1, LH1, and HH1. Again we apply DWT to the HL1 sub-band to get sub-band HL2 or to the HH1 sub-band to get sub-band HH2. This algorithm is a non-blind watermarking algorithm, and thus requires the original image in the extraction process. Therefore, we also decompose the original image using DWT into 4 non-overlapping multi-resolution sub-bands: LL1, HL1, LH1, and HH1. Again, we apply DWT to the HL1 to get sub-band HL2 or to the HH1 to get sub-band HH2. Divide sub-band HL2 (or HH2) of the original and watermarked images into 4 x 4 blocks. Apply SVD to each block in the chosen subband of the watermarked image and extract the singular values matrix SI. Similarly, apply SVD to each block in the sub-band of the original image and extract the singular values matrix S2. Find the difference between all singular values in SI and S2 If the difference exceeds a threshold value of 0.75, take the extracted watermark bit as bit 0, otherwise, take it as bit 1[6]. Reconstruct the watermark using the extracted watermark bits, and compute the similarity between the original and extracted watermarks.

III. COMPARISON Watermarking algorithms are usually evaluated with respect to two metrics: imperceptibility and robustness. Imperceptibility means the perceived quality of the host image should not be distorted by the presence of the watermark. As a measure of quality of a watermarked image, PSNR is typically used. Robustness is a measure of the immunity of the watermark against attempts to remove or degrade it, intentionally or unintentionally, by different types of digital image processing, like image compression, Gaussian noise and Image cropping. A size of 512 × 512 gray image is selected as the cover image and a size of 32 × 32 binary image is selected as watermark. The presented algorithms are simulated by programming in Mat lab 7.14. Peak signal to noise ratio (PSNR) is used to measure the invisibility of the embedded watermark and normalized cross-correlation (NCC) is used to measure the similarity between the extracted watermark and the original watermark. = 10 log (

=

1

255

[ ( , )−

= Where C(m,n) is the cover image

)

( , )]

∑ ∑ (, ) (, ) ∑ ∑ | ( , )|


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CW(m,n) is watermarked cover image W(i,j) is original watermark WR(i,j) is recovered watermark

IV.

RESULTS Cover Image

Watermarked Image

Watermark Image

Recovered watermark

Figure: Digital Image Watermarking (using DCT-SVD and DWT-SVD) Table: Comparing Robustness and imperceptibility of DWT-SVD and DCT-SVD DCT-SVD PSNR 49.51 MSE 0.7685 NCC 0.9810

DWT-SVD 47.29 1.2317 0.9723

V. CONCLUSION In this paper we have compared the combinations of algorithms DWT-SVD and DCTSVD and to simulate results we have used SCILAB. Comparison is done on algorithms in terms of robustness whose values are placed in above table. Accordingly, we have concluded that DCT-SVD method is more robust and best method of digital image watermarking.

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ACKNOWLEDGEMENT Our Heartfelt thanks to K.B .Neelima ,Bharath University for her extending her support.

VI. REFERENCES [1]

[2]

[3]

[4] [5]

[6]

[7]

Alexander Sverdlov, Scott De×ter, Ahmet M. Eskicioglu, ROBUST DCTSVD DOMAIN IMAGE WATERMARKING FOR COPYRIGHT PROTECTION: EMBEDDING DATA IN ALL FREQUENCIES. Chung K,Yang W ,Huang Y, Wu S, Hsu Yu- Chiao, “On SVD-based watermarking algorithm” Applied Mathematics and Computation Elsevier, 188, 54-57, 2007. J.R. Hernandez, M.Amado, and F. Perez- Gonzalez, "DCT-Domain Watermarking Techniques for Still Images: Detector Performance Analysis And a New Structure", in IEEE Trans. Image Processing, vol. 9, pp 55-68, Jan. 2000. F.A.P.Petitcolas, et al., ”Information Hiding - A Survey”, Proceedings of the IEEE, Vol.87, No.7, July 1999, pp.1062-1078. M.M Yeung, et al. ”Digital Watermarking for High- Quality Imaging”, IEEE First Workshop on Multimedia Signal Processing, June23-25 1997, Princeton, New Jersey, pp. 357-362. E. Ganic and A. M. Eskicioglu, "Robust embedding of visual watermarks using discrete wavelet transform and singular value decomposition," Journal of Electronic Imaging, vol. 14, no. 4, Dec 2005. “Digital Image Processing using Matlab” by Rafael C.Gonzalez and Richard E.Woods. Second Edition.


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ABOUT AUTHORS

RajiPanduranngan received B.Tech Degree in year 2000 at Bapatla Engineering College, Andhra Pradesh and M.E Applied Electronics from Sathyabama University, Chennai in the year 2011, and pursuing Ph.D. degree in Digital image processing at Bharath University,Chennai. Current working as Assistant Professor at Bharath University. Her area of interests include Digital watermarking and cryptographic techniques, microprocessors and microcontrollers, VLSI Design.

Dr.E.Logashanmugam received his Doctorate Degree in Electronics Engineering faculty having Twenty one years of experience in Teaching and Research with expertise in the area of Video Signal Processing, currently working as Head/Faculty of Electrical and Electronics ,Sathyabama University, Chennai, India.

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