Hybrid Image Watermarking Using Iwt-Svd

Transcription

1 Hybrid Image Watermarking Using Iwt-Svd 1 Prerna Gupta and 2 Girish Parmar Department of Electronics Engineering, Rajasthan Technical University, Kota, Rajasthan ABSTRACT Watermarking is a process to protect secret digital images from illegal copy and manipulations because digital data can be easily copied and transferred to another user without loss of data and quality of data. To achieve this goal, in this work, an adaptive and non-blind image watermarking algorithm based on Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD)has been presented in order to protect the water. In this technique, a watermark is embedded into the low frequency sub-band of a cover image by using variable scaling factor. The insertion and extraction of the watermark in the cover image has been found to be simpler than other transform techniques. The experimental results show the effectiveness of presented image watermarking scheme. Performance of the methodology has also been evaluated using different fidelity parameters such as; peak signal to noise ratio (PSNR) and normalized cross correlation (NCC). Keywords: IWT, SVD, PSNR, NCC 129 I. INTRODUCTION In present digital era, the ubiquitous network environment has promoted the rapid delivery of digital multimedia data. Users are eager to enjoy the convenience and advantages that networks have provided. Meanwhile, users are eager to share various media information in a rather cheap way without awareness of possibly violating copyrights.therefore, authentication, information security and other various issues are raised with multimedia sources and content. Digital data can be stored efficiently and with a very high quality, and it can be manipulated very easily using computers. These information, which include still images, video, audio, or text are stored and transmitted in a digital format. Information stored in digital format can be easily copied without loss of quality [1]. The digital watermark is then introduced to solve this problem. Digital watermarking is a branch of information hiding which is used to hide proprietaryinformation in digital media like; photographs, digital music, or digital video [2-4]. The ease with which digital content can be exchanged over the internet has created copyright infringement issues. Copyrighted material can be easily exchanged over peer-to-peer networks, and this has caused major concerns to those content providers who produce these digital contents. Generally, the image watermarking can be done in spatial domain or in transform domain [5]. Compared to spatial domain techniques,frequencydomain watermarking techniques proved to be more effective with respect to achieving the imperceptibility and robustness requirements of digital watermarking algorithms [6-8]. Commonly used frequency-domain transforms include the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) andinteger Wavelet Transform (IWT). However, DWT has been used in digital image watermarking more frequently due to its excellent spatial localization and multi-resolution characteristics, which are similar to the theoretical models of the human visual system. Further, performance improvements in DWT-based digital image watermarking algorithms could be obtained by increasing the level of DWT.Here, in the present work, IWT with SVD has been used. IWT has better computational efficiency than DWT. Multimedia contents store integer values. DWT does floating point transformation and hence, inverse DWT truncates the floating point values to integer values. But IWT performs lossless decomposition and hence, it can be used for lossless data hiding. SVD is then performed on the transformed image, as SVD is more robust against any attacks then traditional methods. It has the unique property that even large variations in the singular values do not affect the signal energy. IWT II. IWT and SVD IWT is used for lossless compression. The transform coefficients are represented by finite precision numbers, and this allows for truly lossless coding. IWT is much faster than the DWT because the floating point wavelet transform demands for longer

2 data length than the integer wavelet transform. Reversibility is another benefit of integer transform. Therefore, the image can be reconstructed without any loss because all the coefficients are integers and can be stored without rounding off errors. IWT is implemented using the lifting scheme (LS). Its main advantage with respect to filter bank structure lies in its better computational efficiency and in fact, it enables a new method for filter design. LIFTING SCHEME The lifting scheme is an effective way of implementation of the wavelet filtering which also improves the speed of wavelet transform. IWT uses lifting scheme suggested by W. Sweldens in 1999 for building wavelet completely in spatial domain [7]. Lifting scheme is divided into three stages; Split, Predict and update. Split: Suppose sj 1 is main signal that divided two sample even and odd sets. Split(sj 1)=(sj,dj) (1) sj=s (j 1, 2k) dj = s (j 1, 2k+1) Predict: In this phase, the odd samples are predicted from the even samples. This step is also called dual lifting. If P is a good predictor, dj details are depended to wavelet coefficient. Update: This step is also called primal lifting. In this step, new even samples are produced by adding the original even samples to the predicted odd samples after update them using the updating operator. sj=sj+u(dj) (2) Inverse lifting transform is similar with equation (1), with the difference that its signs are reversed and above actions repeated reversely. sj=sj u(dj) (3) dj=dj+p (sj) merg (sj 1) = (sj, dj) The lifting scheme is a simple method for designing customized biorthogonal wavelets and offers several advantages: 1) Allows a faster implementation of the wavelet transform. 2) Saves storage by providing an in- place calculation of the wavelet Transform. 3) Simplifies the determination of the inverse wavelet transform. SVD From the discernment of image processing, an image can be viewed as a matrix with non-negative scalar entries. SVD is an effective numerical analysis tool from linear algebra to decompose a rectangular matrix A into an orthogonal matrix U, diagonal matrix S, and the transpose of an orthogonal matrix V. SVD decomposes a given image A of size M N as A =USV T Where, U and V are orthogonal matrices of size MxM and NxN, respectively. S is a diagonal matrix of size MxN having singular values. It is worth noting that, the singular vectors of an image specify the image geometry similarly, left singular vectors represent horizontal details and right singular vectors represent the vertical details of an image, while the singular values specify the luminance (energy) of the image. Slight variations in the singular values do not affect the visual perception of the quality of the image. III. WATERMARKING TECHNIQUE Here, an adaptive second level hybrid image watermarking techniqueusing IWT-SVD in low frequency band has been presented.the algorithm is divided into two parts; watermark embedding and watermark extraction. Watermark Embedding The watermark embedding process is described as: Step.1: Load the cover image and water. Step.2: Decomposed both the images into four subbands using IWT for cover and waters, respectively. Step.3: After taking IWT,decomposeboth the images using SVD. Step.4: Compute new sigma matrix using fusion of both sigma matrix. Step.5: Using new computed signal matrix Snew, New LL band is computed with inverse SVD. Finally, watermarked image obtained using inverse IWT based on LLnew band and remainingsub bands of cover image. Watermark Extraction Watermark extraction process is also very important process as it gives the hidden information 130

3 from the watermarked image embedded into cover image. The watermark embedding process is described as: Step.1: Load the cover image, water and watermarked image. Step.2: Decomposed the images into sub-bands using IWT,respectively. Step.3: After taking IWT, decompose the images using SVD. Step.4: Compute new sigma matrix using fusion of both sigma matrixand scaling factor as key in watermark embedding process. Step.5: Using new computed signal matrix Snew, New LL band is computed with inverse SVD. Therefore, extracted water obtained using inverse IWT based on LLnew band and reaming sub band. IV. RESULTS AND DISCUSSION Over all the analysis has been carried with 512X512 images and the fidelity parameters have also been calculated.here, the images used are obtained from USC-SIPI image database which is a standard evaluation database for watermarking algorithms. FIDELITY EVALUATION PARAMETERS The visual performance of watermarked images has been determined by using peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) which is historically adopted in image processing in order to evaluate the performance of the output results, as given in Table I. N M 1 MSE ( f ( i, j) g( i, j)) NM i 1 j 1 2 L PSNR 10log10 MSE (4) Here, L shows the values of pixel range. As MSE is inversely proportional to PSNR, thus the small mean square error tends to high signal to noise ratio. For better image quality, the PSNR must be high. The quality of the image is measured using normalized cross co-relation (NCC) which is given by: NCC i 1 j 1 N M N M 2 2 N M g( i, j) g '( i, j) ( g( i, j)) ( g '( i, j)) i 1 j 1 i 1 j 1 (5) Here, PSNR considered for good efficiency which is close to 35 db in order to avoid a visible watermark but at the same time including the watermark with a large energy to be resistant to attacks. SIMULATION RESULTS COVER IMAGE Fig. 1.Lenaas Cover image. WATERMARK IMAGE Fig. 2.Camerman as Water Figure 1 shows the original cover image whereas in Fig. 2 the cameraman as water has been taken for embedding of watermark in the cover image.table I gives thevalues of PSNR and NCC for 1 level of IWT-SVDbased image watermarking technique. Table I: Values of PSNR and NCC for different S.F. Scaling Factor WatermarkedImage α PSNR (db) NCC

4 PSNR Vs Scaling Factor PSNR Fig.3.PSNR vs S.F. Bar chart in Fig. 3 shows the value of PSNR at different values of scaling factor.thevalue of scaling factor αis varied from 0.01 to1. It can be seen that, as the value of visibility factor decreases, the value of PSNR increases but at the same time the value of CCdecreases hence to get the best resultwe set the value of visibility factor at Fig. 4. Watermarked image & recover water (α= 0.025) (best result). Fig. 5. Watermarked image & recover water (α=1.0). Fig. 6. Watermarked image & recover water (α= 1.0). Figures 4-6 show the result of watermarked image and recover water at different scaling factor; α. The simulated experimental results have also been evaluated with visual representation of watermarked and extracted water for human vision system (HVS). V. CONCLUSIONS In this paper, a non-blind watermarking scheme based on IWT and SVD has been implemented.modifying singularvalues of the host image in IWT domain provides high robustness and simultaneouslyalso increases the value of transparency. This technique can embed the watermark into salient features of the image using variable scaling factor. Experimental results show the quality of the watermarked image and the recovered watermark. All the results obtained for the recovered images and the watermark are identical to the original images.the methodology is having robust efficiency of watermarking with data hiding ability. REFERENCES [1] W. Bender, D. Gruhl, N. Morimoto and A. Lu, "Techniques for Data Hiding",IBM System Journal, Vol. 35, NOS 3&4, pp , [2] E. T. Lin and E. J. Delp, A Review of Data Hiding in Digital Images,Proc. of the Image Processing, Image Quality, Image Capture Systems Conf. (PICS 99), pp , [3] P. H. W. Wong, O. C. Au and G. Y. M. Yeung, A Novel Blind Multiple Watermarking Technique for Images,IEEE Transactions on Circuits and Systems for Video Technology: Special Issue on Authentication, Copyright Protection and Information Hiding, Sept [4] S. Lingamgunta, V. K. Vakulabaranam, and S. Thotakura, Reversible watermarking for image authentication using IWT, International Journal of Signal Processing, Image Processing & Pattern Recognition, Vol. 6, No. 1, [5] A. P. Singh., A. Mishra A., Wavelet Based Watermarking on Digital Image, Indian Journal of Computer Science and Engineering, [6] L. Hu, F. Wan, Analysis on wavelet coefficient for image watermarking, IEEEInternational Conference onmultimedia Information Networking and Security (MINES),pp , [7] W. Sweldens, The lifting scheme: a construction of second generation wavelets, 132

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