Resistance of Watermarked Image against Solidity Attacks
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1 International Journal of Scientific Research in Research Paper. Computer Science and Engineering Vol.5, Issue.4, pp.31-35, August (217) E-ISSN: Resistance of Watermarked Image against Solidity Attacks S. Mudassar 1*, M. Jamal 2, F. S. Mehmood 3, M. R. Rehman 4 1* Dept. of Computer Sciences, NCBAE, Multan, Pakistan 2 Dept. of Computer Sciences, NCBAE, Multan, Pakistan 3 Dept. of Computer Sciences, NCBAE, Multan, Pakistan 4 Dept. of Computer Sciences, Govt. Sector, Multan, Pakistan * Corresponding Author: sairamcs4@gmail.com, Tel.: Available online at: Received 1 th Jun 217, Revised 16 th Jul 217, Accepted 12 th Aug 217, Online 3 th Aug 217 Abstract Many techniques are implemented to secure digital contents with the passage of time. Watermarking is the best technique to provide security for multimedia contents. It can also use for the stoutness and authentication of digital images against planned and unplanned attacks during the inserting and extracting procedure of watermarking into host/cover digital image. Singular Value Decomposition (SVD) and Discrete Wavelet Transformation (DWT) is non-blind watermarking procedure because this technique efficiently insert information into the original contents without loss the quality. My fine objective is to use hybrid DWT-SVD based colour image watermarking. According to this technique non authorized user cannot visit the hidden information. Using DWT-SVD method the colour channels of host and watermark images were divided into R, G and B sub-channels. For inserting watermark image, the R channel of both cover image and watermark image were integrated by secret alpha key. Image can also be extracted from the watermarked image by applying reverse procedure. Different parameters are implemented for the quality of watermarked and extracted watermark image in experimental result. Keywords PSNR, SSIM, DWT, SVD, NC. I. INTRODUCTION The ubiquitous nature of digital network systems means huge numbers of people can be copied digital contents without any cost. People can easily download images, videos and audio files and distribute them with other peoples or may also be modify the original contents. So there is crucial requirement to provide protection of such digital media. There are many technologies to evaluate such problems like digital signatures. But these are is a problematic solutions because they require more bandwidth. Due to some limitations of previous technologies, a technique came into being and this technique is called digital watermarking. Watermarking algorithms are implemented to embed digital content to provide to provide copyright protection and ownership identification. Many commercial firms round the whole world give the facility of copyright protection to their clients. The watermark may be noticeable or imperceptible, noticeable watermark is seen by the observer and invisible watermark only identified by some decoding processes. For these reason, the watermark must be robust enough so that nobody can destroyed or changed the digital content. Security is a main problem which requires that watermark can be altered by the actual owner. DWT To hierarchically decompose an image discrete Wavelet transform (DWT) is used because it is a mathematical tool for image decomposition. It is useful for Non-stationary signals. The transform produce small waves known as wavelets, that varying frequency. Spatial and frequency detail are also provided by this technique. Mother wavelet is a secure function that is implemented to create wavelets details and their translation. DWT is use for the image description in multi resolution form. Decoding can be implemented in order steps from a low resolution to the higher resolution. The DWT separate the signals into high and low frequency sections. Information related to edge components exist in high frequency section, and the low frequency section is selected again for splitting into high and low frequency sections. The components having low frequency are mostly selected for watermarking. In two dimensional decomposition, on every level DWT is implemented in vertical as well as horizontal direction. First stage of decomposition, there are resulted four sub-bands namely LL1, LH1, HL1, and HH1. For further stage of decomposition, the LL sub band of the DWT1 stage is implemented for 2DWT decomposition as an input which decompose the LL1 band into the four sub- bands LL2, LH2, 217, IJSRCSE All Rights Reserved 31
2 Int. J. Sci. Res. in Computer Science and Engineering Vol-5(4), Aug 217, E-ISSN: HL2, and HH2. Similarly decomposition of third level is applied on LL2 with the help of DWT and received four sub bands LL3, LH3, HL3, and HH3. LH1, HL1, and HH1 are considered to be highest frequency bands in the image decomposition, while LL3 considered to be the lowest frequency band. Fig 1 Image decomposing with DWT SVD To solve many difficult mathematical problems a simple linear algebra technique is used which is called Singular value decomposition.this technique is also used in digital image watermarking for embedding and extraction process. Images are considered to be square matrix using SVD, without the loss of image quality. So SVD technique can be easily implemented to any kind of digital images either the images may be grayscale or RGB. The orthogonal transform belongs to SVD which can decompose the given matrix into three equal size of matrixes of same size from which one matric is called diagonal and others two are orthogonal. Diagonal matrix are used in digital image watermarking technique to embed the watermark into the original digital content. Square matrix are not require to decompose the matrix using SVD technique. For example if we have image that can be represented by matrix A. So, the decomposition if matrix A can be represented by using the equation (1) A=USV T U and V U*U T = I V*V T = I represent the unitary matrices Where, I represent the Identity matrix A USV S is the diagonal matrix having in its main diagonal all nonnegative singular values of A. These positive singular values can be used to embed watermark. The order of singular matrix S is same as original matrix A. Fig 3 Extraction Procedure The parameters Peak to Noise Ratio (PSNR), Normalized Correlation (NC), Mean Square Error (MSE), Structural Similarity Index (SSIM), Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR) are used to measure the quality of images produced in the embedding procedure or extracted procedure. A higher PSNR would normally indicate that the reconstruction of image in embedding or extraction procedure is higher quality. PSNR is calculated by the given formula MAX I =Maximum value of pixel in Original image m=no. of Row in Original image n= No. of Column in Original image Where MSE is simply the mean square error between the original and corrupted signals III. RESULTS AND DISCUSSION II. METHODOLOGY Host Image (Ali Hassan) Watermark Image (Tomb) Watermarked Image Extracted Watermark Fig 2 Embedding Procedure Table 1 217, IJSRCSE All Rights Reserved 32
3 Int. J. Sci. Res. in Computer Science and Engineering Vol-5(4), Aug 217, E-ISSN: Hostmand watermarked image Extractedmand watermark image PSNR NC MSE SSIM RSME SNR Values for Host and watermarked image, Extracted watermark PSNR NC MSE SSIM RSME SNR Host and watermarked image Extracted and watermark image Fig 4 comparision Table 2 Hostmand watermarked image Extractedmand PSNR(red) PSNR(green) PSNR(blue) NC(red) NC(green) NC(blue) Values for RGB channels watermark image Table 3 Attacks PSNR MSE SSIM RMSE Salt & Pepper Mean Filter GaussianNoise Average Filter Median Filter Sharpen Rotate Robustness of Watermarked image against different attacks Fig 5 Attacks Implementation for Watermarked image Table 4 Attacks PSNR MSE SSIM RMSE Saltm& Pepper Mean Filter Gaussian noise Average filter Median Filter Sharpen Rotate PSNR MSE SSIM RMSE Robustness of Extracted Watermark image against different attacks Host and watermarked image Extracted and watermark image Fig 5 Comparision of RGB channels values PSNR MSE SSIM RMSE Fig 6 Attacks implementation for extracted watermark image 217, IJSRCSE All Rights Reserved 33
4 Int. J. Sci. Res. in Computer Science and Engineering Vol-5(4), Aug 217, E-ISSN: Histogram Equalization Host and watermark image Sharpness, Median Filter, Average Filter after the insertion and extraction procedure of watermarking. The superiority of watermarked image was same as the host image. Fig 7 Histogramm Equalization Watermark and extracted watermark image Fig 8 Histogramm Equalization Watermark and extracted watermark image Fig 9 Histogramm Equalization IV CONCLUSION Hybrid DWT-SVD watermarking is one of the best technique to provide security for multimedia images. It has improved the robustness of watermarked digital images. These images showed strong protection against planned and unplanned geometric attacks like Salt and Pepper, Gaussian, REFERENCES [1] A.Khalid, Al-Afandy1, EL-Sayed M. EL-Rabaie2, Fathi E. Abd El-Samie2, Osama S. Faragallah1, Ahmed ELmhalaway1, A. M. Shehata1 "Robust Color Image Watermarking Technique Using DWT and Homomorphic based SVD" Year 216 [2] Md. Atiqur Rahman & M. M. Fazle Rabbi Non-Blind DWT-SVD based Watermarking Technique for RGB Image Volume 15 Issue 4 Version 1. Year 215. [3] M.Barni., E Bartolini, V. Cappellini, and A. Piva, A DCTdomain system for robust image watermarking, Signal Processing Vol. 66, No. 3, pp , May [4] Suhad Hajjara, Moussa Abdallah, Amjad Hudaib, Digital Image Watermarking Using Localized Biorthogonal Wavelets European Journal of Scientific Research, ISSN X Vol.26 No.4, pp , 29. [5] Ahmed Salama, Randa Atta, Rawya Rizk and Fayez Wanes 211, A Robust Digital Image Watermarking Technique Based on Wavelet Transform. ICSET 211 [6] Based on Hybrid Fractal-Wavelet 211 International Conference on Computer Communication and Management Proc. of CSIT vol.5 (211) IACSIT Press, Singapore [7] Sanaz Shahraeini and Mahdi Yaghoobi 211. A Robust Digital Image Watermarking Approach against JPEG Compression Attack. [8] S.Poonkuntran, R.S.Rajesh 211. A Messy Watermarking for Medical Image Authentication. 211 IEEE. [9] J.Mishra, M. V. Patil, and J. S. Chitode, An effec- tive audio watermarking using DWT-SVD, Inter- national Journal of Computer Applications, vol. 7, no. 8, pp. 6 11, 213. [1] A.Sleit., S. Abusharkh, R. Etoom, and Y. Khero, An enhanced semi-blind DWT - SVD based water marking technique for digital images, The Imaging Science Journal, vol. 6, no. 1, pp , 212. [11] B.Surekha and G. N. Swamy, Sensitive digital im- age watermarking for copyright protection, Inter- national Journal of Network Security, vol. 15, no. 2, pp , 213. [12] K.Navas, M. C. Ajay, M. Lekshmi, T. S. Archana, and M. Sasikumar, DWT-DCT-SVD based wa- termarking, in Third International Conference on Communication Systems Software and Middleware and Workshops, pp , 28. [13] B.Surekha and G. N. Swamy, Sensitive digital im- age watermarking for copyright protection, Inter- national Journal of Network Security, vol. 15, no. 2, pp , 213. [14] A.Rajani,Dr.T.Ramashri, IMAGE WATERMARKING ALGORITHM USING DCT, SVD AND EDGE DETECTION TECHNIQUE, Interna-tional Journal of Engineering Research and Applications(IJERA) ISSN: Vol. 1, Issue 4, pp [15] Prabhishek Singh, R S Chadha A Survey of Digital Watermarking Techniques, applications and Attacks International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 9. Authors Profile Miss. S. Mudassar pursed Master in Computer Science from Bahauddin Zakariya University, Multan in 212 and Master of Science from National College of Business Administration and Economics in year 217.She is currently working as Lecturer in 217, IJSRCSE All Rights Reserved 34
5 Int. J. Sci. Res. in Computer Science and Engineering Vol-5(4), Aug 217, E-ISSN: Department of Computer Sciences since 213. She has published more than 1 research papers in reputed international journals, also available online. Her main research work focuses on Watermarking, Cryptography and Stegnography Algorithms, Network Security, and Privacy. She has 3 years of teaching experience and more than 2 years of Research Experience. Miss. M. Jamal pursed Bachelor of Science from Bahauddin Zakariya University, Multan in 212 and Master of Science from National College of Business Administration and Economics in year 217.She is currently working as Lecturer in Department of Computer Sciences since 213. She has published more than 1 research papers in reputed international journals, also available online. Her main research work focuses on Watermarking, Cryptography and Stegnography Algorithms, Network Security, and Privacy. She has 3 years of teaching experience and more than 2 years of Research Experience.. 217, IJSRCSE All Rights Reserved 35
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