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Embedded blind image watermarking using wavelet transforms and singular value decomposition

Embedded blind image watermarking using wavelet transforms and singular value decomposition






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    Embedded blind image watermarking using wavelet transforms and singular value decomposition Embedded blind image watermarking using wavelet transforms and singular value decomposition Document Transcript

    • Embedded Blind Image Watermarking using Wavelet Transform and Singular Value Decomposition A. Vignesh Vetri Vel1 , R. Shanthakumari2 , B. Venkatesan3 1 Department of Information Technology 2 Department of Information Technology 3 Department of Electronics and Instrumentation Engineering Kongu Engineering College, Perundurai, Erode – 638052. Email id: vetriit4@gmail.com Abstract—Digital image watermarking is one of the latest techniques which are used for safeguarding the image by protecting it against being secluded. Protection of any multimedia data has became an increasingly an important issue for the owner. Watermark is embedded inside an image to show authenticity or proof of ownership and it provides copyright protection. The idea behind developing a digital image watermarking technique is to satisfy both imperceptibility and robustness requirements. A watermarking scheme based on wavelet transform (WT) and singular value decomposition (SVD) is proposed to achieve the requirements of a digital watermarking technique. The image that is to be watermarked is embedded into the cover image by using WT and SVD. The extraction is performed on the watermarked image by using WT and SVD. The result will be an image which will be robust and will satisfy the imperceptibility requirements. Structural Similarity Index Measure (SSIM) is used to verify the robustness of the extracted image. Keywords—Discrete Wavelet Transform, Stationary Wavelet Transform, Singular Value Decomposition, MSSIM, Watermarking. I. INTRODUCTION The term digital watermark is used to describe that which enables differentiation between copies of the same content in an imperceptible manner. Watermarking is the process of hiding digital information in a carrier signal; the hidden information need not contain a relation to the carrier signal. Digital watermarks may be used to verify the authenticity or integrity of the carrier signal or to show the identity of its owners. It is prominently used for tracing copyright infringements and for banknote authentication. Traditional Watermarks may be applied to visible media like images or video. Watermarking can be divided into two main groups: 1) Visible Watermarks and 2) Invisible Watermarks. A visible watermark is a visible semi- transparent text or image overlaid on the original image. It allows the original image to be viewed, but it still provides copyright protection by marking the image as its owner’s property. Visible watermarks are more robust against image transformation. Thus they are preferable for strong copyright protection of intellectual property that’s in digital format. An invisible watermark is an embedded image which cannot be perceived with human’s eyes. Only electronic devices or specialized software can extract the hidden information to identify the copyright owner. Invisible watermarks are used to mark specialized digital contents like text, images or even audio content to prove its authenticity. Robustness, Imperceptibility and security are the main characteristics of Digital Watermarking. Robustness is the ability to withstand after normal signal processing applications such as image cropping, compression etc. Imperceptibility is defined by visibility, so that the watermarked image should look like same as the original image to the normal eye. Hence the viewer cannot detect that the watermark is embedded in it. Security is the protectively of detection, retrieve or modification from the unauthorized person. Wavelet Transforms and Singular Value Decomposition are playing a vital role in the field of watermarking [1] and [2]. SWT is performing better in watermarks which resist the noise, also embed the watermark content as much as possible without affecting the image [3]. A watermarking system is usually divided into three distinct steps, embedding, attack and extraction. In embedding, an algorithm accepts the host and the data to be embedded and produces a watermarked signal. The watermarked signal is then transmitted or stored, usually transmitted to another person. If this person makes a modification, this is called an attack. There are many possible attacks. Extraction is an algorithm which is applied to the attacked signal to attempt to extract the watermark from it. If the signal was not modified during transmission, then the watermark is still present and it can be extracted. If the signal is copied, then the information is also carried in the copy. The embedding takes place by manipulating the content
    • of the digital data, which means the information is not embedded in the frame around the data, it is carried with the signal itself. The obtained watermarked image is passed through a decoder in which usually a reverse process is employed during the embedding stage is applied to retrieve the watermark. II. PROPOSED METHODOLOGY By going through the above literatures DWT, SWT and SVD based watermarking is proposed. A. Embedding In the embedding process we have used single level of WT for the cover image to get four sub-bands such as LL, LH, HL and HH with help of haar coefficients. Then SVD is calculated for the LH and HL sub-bands. Then the watermark image is divided into two parts. A scaling factor is used to control the strength of the watermark image. Thus scaling factor is multiplied with each part of the watermark image and then added with the singular values of the corresponding sub-band. Then SVD is calculated for the modified value. Then modified HL and LH sub-band values are obtained using the modified singular values and the unmodified scalar values. Finally, inverse WT is made on the LL, modified LH, modified HL and HH sub-bands to obtain the watermarked image. Embedding process is shown in Figure 1. Figure 1. Watermark embedding process B. Extraction In the extraction process WT is performed on the watermarked image to obtain the LL, LH, HL and HH sub-bands. SVD values are calculated for the HL and LH sub-bands. Compute the value D obtained by multiplying the singular values in LH and HL and sub-bands with the modified scalar values in the embedding process. Subtract D from the singular values of the LH and HL sub-bands and divide with the scaling factor as in the embedding process to obtain the two parts of the watermark image. Thus, the obtained parts of watermark image are added to obtain the original watermark image. Extraction process is shown in Figure 2. Figure 2. Watermarked image extraction process III. WAVELET TRANSFORMS Wavelet transform is capable of providing the time and frequency information simultaneously, hence giving a time-frequency representation of the signal. A. Discrete Wavelet Transform(DWT) The Discrete Wavelet Transform DWT is obtained by filtering the signal or image through a series of digital filters at different scales. The scaling of the signal is done by changing the resolution of the signal by the process of sub sampling. The DWT can be calculated by convolution based methods. In this method, the input sequence is decomposed into low pass and high pass sub-bands each consisting of half the number of samples in the original sequence. Thus the applications of DWT are Compression, De- noising, Transmission and Characterization. Hence it requires only less computation [4]. Wavelet transform decomposes an image into a set of band limited components which can be reassembled to reconstruct the original image without error. Since the bandwidth of the resulting sub-bands is smaller than that of the original image, the sub-bands can be down sampled without loss of information. Reconstruction of the original signal is accomplished by up sampling, filtering and summing the individual sub bands. Figure 3 shows the block diagram of discrete wavelet transform. Figure 3. Block Diagram of DWT The DWT can be implemented as a multistage transformation. At first level, the image is decomposed into four sub-bands denoted LL, LH, HL, and HH. Here LH, HL, and HH represent the finest scale wavelet coefficients and LL stands for the coarse-level coefficients. The LL sub-band
    • can further be decomposed to obtain another level of decomposition. The decomposition process continues on the LL sub-band until the desired number of levels determined by the application is reached. Since human eyes are much more sensitive to the low-frequency part the LL sub- band, the watermark can be embedded in the other three sub-bands to maintain better image quality. B. Stationary Wavelet Transform(SWT) In case of stationary wavelet transform the down-sampling is not carried out. Hence low pass and high pass sub-bands consisting of same number of samples in the original image. The main application of the SWT is de-noising. Figure 4 shows the block diagram of stationary wavelet transform [5]. Figure 4. Block diagram of SWT IV. SINGULAR VALUE DECOMPOSITION The singular value decomposition SVD is a factorization of a real or complex matrix. Any n x m matrix A can be written in the form A = USVT where U is an orthogonal n x n matrix, V is an orthogonal m x m matrix, and S is an n x m matrix whose first r diagonal entries are the non-zero singular of A and whose other entries are all zeros. The expression USVT is known as the Singular Value Decomposition of A. From the perspective of image processing, an image can be viewed as a matrix with nonnegative scalar entries. The SVD of an image A with size m x m is given by A = USV T, where U and V are orthogonal matrices, and S = diag(λi) is a diagonal matrix of singular values λi, where i = 1, . . . , m, which are arranged in decreasing order. The columns of U are the left singular vectors, whereas the columns of V are the right singular vectors of image A. The basic idea behind the SVD-based watermarking techniques is to find the SVD of the cover image or each block of the cover image, and then modify the singular values to embed the watermark. There are two main properties to employ the SVD method in the digital-watermarking scheme: 1) when a small perturbation is added to an image, large variation of its singular values does not occur. 2) Singular values represent intrinsic algebraic image properties [6]. Figure 5 shows the block diagram of SVD. Figure 5. Block diagram of SVD V. QUALITY METRICS A. Structural Simillarity Imdex Measure Structure similarity is the quality measurement between the original and reconstructed image. It can be used to distinguish between structural and non-structural distortions, giving results that agree with perception for very strongly distorted images (supra-threshold distortions). The structural similarity metric gives a result ranging from 0.0 to 1.0, where zero corresponds to a loss of all structural similarity and one corresponds to having an exact copy of the original image. Images with lighting-related distortions give high SSIM while other distortions result in low similarity. SSIM is used to calculate with the help of the below calculation, which performs better than PSNR [7] and [8]. Where, µx – Mean Intensity of Reference Image µy – Mean Intensity of distorted Image σ – Standard Deviation c1, c2 – Constants VI. ATTACKS Digital watermarking is not as secure as data encryption. Therefore, digital watermarking is not immune to hacker attacks [9]. Some of the attacks are explained below [10]: A. Histogram Equalization(HE) A specific attack is there which cannot be visually seen in the initial stage but becomes visible when deepen further and that attack is histogram equalization. It is the process in which intensity of the pixels are reassigned in such a way so that all the values available for assigning a gray level are utilized. This technique is generally used to enhance the image, so it can be an attack for the image watermark. B. Contrast Adjustment Contrast is the difference in brightness between objects or regions. The contrast adjustment is a way of zooming in on a smaller range of pixel values. C. Median Filtering Median filtering is a nonlinear process useful in reducing impulsive or salt-and-pepper noise. It is also useful in preserving edges in an image while reducing random noise. Impulsive or
    • salt-and pepper noise can occur due to a random bit error in a communication channel. In a median filter, a window slides along the image, and the median intensity value of the pixels within the window becomes the output intensity of the pixel being processed. D. Average Filtering Average filter is windowed filter of linear class, that smoothes signal (image). The filter works as low-pass one. The basic idea behind filter is for any element of the signal (image) take an average across its neighborhood. VII. RESULTS AND DISCUSSIONS Several experiments are presented to demonstrate the performance of the proposed approach. The gray-level image Lena is used as the cover image and Cameraman is used as the watermark image. Figure 6 illustrates the resultant watermarked images by using DWT and SWT. It can be observed that the approach preserves the high perceptual quality of the watermarked image. (a) (b) (c) (d) (e) Figure 6. Resultant Images (a) Cover Image (b) Watermark Image (c) Watermarked Image (d) Watermark Extracted Image using DWT (e) Watermark Extracted Image using SWT As a measure of the quality of a watermarked image, the structural similarity index measure (SSIM) was used. To evaluate the robustness of the proposed approach, the watermarked image was tested against different kinds of attacks: 1) Denoising attack: average filtering (AF); 2) image-processing attack: histogram equalization (HE), contrast adjustment (CA) and 3) Median Filtering. For comparing the similarities between the original and extracted watermarks, the structural similarity index measure was employed. In the experiments, the values of the scale factors are carried out with constant range from 0.01 to 0.09 with an interval of 0.02, and the results are illustrated in Tables 1 and 2. It can be seen that the larger the scale factor, the stronger the robustness of the applied watermarking scheme. In contrast, the smaller the scale factor, the better the image quality. TABLE 1. ROBUSTNESS COMPARISION FOR DIFFERENT SCALING FUNCTION VALUE USING DWT TABLE 2. ROBUSTNESS COMPARISION FOR DIFFERENT SCALING FUNCTION VALUE USING SWT Image SF Value Attacks AF HE CA MF Cameraman 0.01 0.9585 0.7644 0.7753 0.8149 0.03 0.9262 0.8102 0.716 0.8330 0.05 0.9223 0.8309 0.7029 0.8508 0.07 0.7818 0.7147 0.5768 0.8207 0.09 0.7503 0.6826 0.5467 0.8005 Baboon 0.01 0.9724 0.8113 0.7370 0.856 0.03 0.9729 0.8389 0.7803 0.8740 0.05 0.9785 0.8537 0.7051 0.8851 0.07 0.9499 0.8836 0.6408 0.8418 0.09 0.9365 0.8908 0.6203 0.8180 Bird 0.01 0.9528 0.7622 0.7788 0.8202 0.03 0.9330 0.8084 0.7341 0.8543 0.05 0.9315 0.8346 0.7142 0.8686 0.07 0.8940 0.8365 0.6558 0.8303 0.09 0.9026 0.8469 0.6607 0.8401 VIII. CONCLUSION In this paper, a hybrid image- watermarking technique based on WT and SVD Image SF Value Attacks AF HE CA MF Cameraman 0.01 0.9553 0.7376 0.7645 0.8671 0.03 0.9776 0.7883 0.7739 0.6697 0.05 0.9874 0.7975 0.7802 0.4719 0.07 0.9925 0.8049 0.7836 0.3936 0.09 0.9935 0.8060 0.7852 0.3397 Baboon 0.01 0.9751 0.7756 0.7326 0.9024 0.03 0.9967 0.7881 0.7379 0.8250 0.05 0.9987 0.7949 0.7447 0.6824 0.07 0.9991 0.8037 0.7480 0.5782 0.09 0.9992 0.9900 0.7497 0.5036 Bird 0.01 0.9541 0.7513 0.7677 0.8709 0.03 0.9785 0.7948 0.7813 0.7641 0.05 0.9865 0.8074 0.7865 0.6192 0.07 0.9905 0.8169 0.7874 0.5215 0.09 0.9922 0.8247 0.7882 0.4487
    • has been presented, where the watermark is embedded on the singular values of the cover image’s DWT sub-bands. The technique fully exploits the respective feature of these two transform domain methods: spatio-frequency localization of WT and SVD efficiently represents intrinsic algebraic properties of an image. Experimental results of the proposed technique have shown both the significant improvement in imperceptibility and the robustness under attacks. Further work of integrating the human visual system characteristics and into our approach is in progress and extraction of watermark using SWT is in progress. REFERENCES [1] G. Bhatnagar and B. Raman, “A new robust reference watermarking scheme based on DWT-SVD,” Comput. Standards Interfaces, Vol. 31, no. 5, pp. 1002–1013, Sep. 2009. [2] Anbarjafari, G., “Image Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition”, IEEE Transactions on Image Processing, Vol. 20, issue 5, pp. 1458 – 1460, May 2011 [3] H.-T. Wu and Y.-M. Cheung, “Reversible watermarking by modulation and security enhancement,” IEEE Trans. Instrum. Meas., Vol. 59, no. 1, pp. 221–228, Jan. 2010. [4] E. Ganic and A. M. Eskicioglu, “Robust DWT-SVD domain image watermarking: Embedding data in all frequencies,” in Proc. Workshop Multimedia Security, Magdeburg, Germany, 2004, pp. 166–174. [5] Dong, Wang Hua, Wei, Huang and Li, Liao “A robust image watermarking algorithm based on stationary multiwavelet transform” International Conference on Computer Application and System Modeling (ICCASM), Vol. 6, pp. 291–294, October 2010. [6] J. R. Liu and T. Tan, “An SVD-based watermarking scheme for protecting rightful ownership,” IEEE Trans. Multimedia, Vol. 4, no. 1, pp. 121–128, Mar. 2002. [7] Alan C. Brooks and Thrasyvoulos N. Pappas,“Structural Similarity Quality Metrics in a Coding Context: Exploring the Space of Realistic Distortions,” IEEE Trans. image processing, Vol. 17, no. 8, pp. 1361–1273 August 2008. [8] Zhou Wang, Alan Conard Bovik, Hamid Rahim, and Eero P. Simoncelli (2004), “Image Quality Assessment: From Error Visibility to Structural Similarity”, IEEE Transaction on Image Processing, Vol. 13, no. 4. [9] Andreja Samcovi and Jan Turan “Attacks On Digital Wavelet Image Watermarks”, Journal Of Electrical Engineering, Vol. 59, no. 3,pp. 131–138, 2008. [10] Chunlin Song, Sudirman, S., Merabti, M. and Llewellyn- Jones, D., “Analysis of Digital Image Watermark Attacks” Consumer Communications and Networking Conference (CCNC) IEEE, pp. 1–5, Jan. 2010.