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A robust SVD-based image watermarking using a multi-objective particle swarm optimization

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Abstrakty
EN
The major objective in developing a robust digital watermarking algorithm is to obtain the highest possible robustness without losing the visual imperceptibility. To achieve this objective, we proposed in this paper an optimal image watermarking scheme using multi-objective particle swarm optimization (MOPSO) and singular value decomposition (SVD) in wavelet domain. Having decomposed the original image into ten sub-bands, singular value decomposition is applied to a chosen detail sub-band. Then, the singular values of the chosen sub-band are modified by multiple scaling factors (MSF) to embed the singular values of watermark image. Various combinations of multiple scaling factors are possible, and it is difficult to obtain optimal solutions. Thus, in order to achieve the highest possible robustness and imperceptibility, multi-objective optimization of the multiple scaling factors is necessary. This work employs particle swarm optimization to obtain optimum multiple scaling factors. Experimental results of the proposed approach show both the significant improvement in term of imperceptibility and robustness under various attacks.
Twórcy
  • Centre de Recherche Développement, Bouchaoui, Algeria
  • Department of Electrical and Computer Engineering, Laval University, 2325 Rue de l’Université, Québec, QC G1V 0A6, Canada
autor
  • Centre de Recherche Développement, Bouchaoui, Algeria
  • School of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast, UK
autor
  • Centre de Recherche Développement, Bouchaoui, Algeria
  • School of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast, UK
Bibliografia
  • 1. K. Tanaka, Y. Nakamura, and K. Matsui, “Embedding secret information into a dithered multi−level image,” in Proc. IEEE. Military Communications Conf. 1, 216–2201 (1990).
  • 2. I.J. Cox, J. Kilian, F. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE T. Image Process. 6, 1673–1687 (1997).
  • 3. J.R. Hernandez, M. Amado, and F. Perez−Gonzalez, “DCT−domain watermarking techniques for still images: detector performance analysis and a new structure,” IEEE T. Image Process. 9, 55–68, (2000).
  • 4. C. Hsu and J. Wu, “Hidden digital watermarks in images,” IEEE T. Image Process. 8, 58–68 (1999).
  • 5. K. Loukhaoukha and J.−Y. Chouinard, “A new image watermarking algorithm based on wavelet transform,” in IEEE Canadian Conf. Electrical and Computer Engineering, 781–786, 2009.
  • 6. W. Lu, W. Sun, and H. Lu, “Robust watermarking based on DWT and nonnegative matrix factorization,” Comput. Electr. Eng. 35, 183–188 (2009).
  • 7. V. Solachidis and I. Pitas, “Circularly symmetric watermark embedding in 2−D DFT domain,” IEEE T. Image Process. 10, 1741–1753 (2001).
  • 8. T.K. Tsui, X.−P. Zhang, and D. Androutsos, “Colour image watermarking using multidimensional fourier transforms,” IEEE T. Information Forensics and Security 3, 16–28 (2008).
  • 9. E. Abdallah, A.B. Hamza, and P. Bhattacharya, “Improved image watermarking scheme using fast Hadamard and discrete wavelet transforms,” J. Electron. Imaging. 16, 1–9 (2007).
  • 10. S.P. Maity and M.K. Kundu, “Perceptually adaptive spread transform image watermarking scheme using Hadamard transform,” Inform. Sciences. 181, 450–465 (2011).
  • 11. P. Campisi, D. Kundur, and A. Neri, “Robust digital watermarking in the Ridgelet domain,” IEEE Signal Process. Lett. 11, 826–830 (2004).
  • 12. H.−Y. Yu, J.−L. Fan, and X.−L. Zhang, “A robust watermark algorithm based on ridgelet transform and fuzzy c−means,” in Int. Symp. Information Engineering and Electronic Commerce, 120–124 (2009).
  • 13. S.P. Mohanty and B.K. Bhargava, “Invisible watermarking based on creation and robust insertion−extraction of image adaptive watermarks,” ACM T. Multimedia Computing, Communications and Applications 5, 12:1–12:22 (2008).
  • 14. K. Loukhaoukha and J.−Y. Chouinard, “Hybrid watermarking algorithm based on SVD and lifting wavelet transform for ownership verification,” in Proc. Canadian Workshop on Information Theory, pp. 177–182, 2009.
  • 15. H.−M. Tsai and L.−W. Chang, “A high secure reversible visible watermarking scheme,” in Proc. IEEE Int. Conf. Multimedia and Expo, pp. 2106–2109, 2007.
  • 16. A. Verma and S. Tapaswi, “A novel reversible visible watermarking technique for images using noise sensitive region based watermark embedding (NSRBWE) approach,” in IEEE Eurocon, pp. 1374–1377, 2009.
  • 17. D. Simitopoulos, D. Koutsonanos, and M. Strintzis, “Robust image watermarking based on generalized radon transformations,” IEEE T. Circuits and Systems for Video Technology 13, 732–745 (2003).
  • 18. V. Aslantas, S. Ozer, and S. Ozturk, “Improving the performance of DCT−based fragile watermarking using intelligent optimization algorithms,” Opt. Commun. 282, 2806–2817 (2009).
  • 19. Y.−J. Chang, R.−Z. Wang, and J.−C. Lin, “A sharing−based fragile watermarking method for authentication and self−recovery of image tampering,” Eurasip J. Advan. Sig. Pr., Article ID 846967, doi:10.1155/2008/846967, 17 pages (2008).
  • 20. M.−J. Tsai and C.−C. Chien, “Authentication and recovery for wavelet−based semi fragile watermarking,” Opt. Eng. 47, 067005 (10 pages) (2008).
  • 21. N. Ishihara and K. Abe, “A semi−fragile watermarking scheme using weighted vote with sieve and emphasis for image authentication,” IEICE T. Fund. Electr., Communications and Computer Sciences E90−A, 1045–1054 (2007).
  • 22. E. Ganic and A. M. Eskicioglu, “Robust embedding of visual watermarks using discrete wavelet transform and singular value decomposition,” J. Electron. Imaging 14, 043004 (2005).
  • 23. R. Liu and T. Tan, “A SVD−based watermarking scheme for protecting rightful ownership,” IEEE T. Multimedia 4, 121–128 (2002).
  • 24. B. Mohan and S. Kumar, “A robust image watermarking scheme using singular value decomposition,” Multimed. Tools Appl. 3, 7–15 (2008).
  • 25. K. Ramanjaneyulu and K. Rajarajeswari, “Wavelet−based oblivious image watermarking scheme using genetic algorithm,” IET Image Process. 6, 364–373 (2012).
  • 26. M. Rohani and A. Avanaki, “A watermarking method based on optimizing SSIM index by using PSO in DCT domain,” in Proc. IEEE Int. CSI Computer Conf., pp. 418–422, Tehran, 2009.
  • 27. H.−H. Tsai, Y.−J. Jhuang, and Y.−S. Lai, “An SVD−based image watermarking in wavelet domain using SVR and PSO,” Appl. Soft Comput. 12, 2442–2453 (2012).
  • 28. X. P. Zhang and K. Li, “Comments on “An SVD−based watermarking scheme for protecting rightful ownership”,” IEEE T. Multimedia 7, 593–594 (2005).
  • 29. R. Rykaczewski, “Comments on an SVD−based watermarking scheme for protecting rightful ownership”,” IEEE T. Multimedia 9, 421–423 (2007).
  • 30. K. Loukhaoukha and J.−Y. Chouinard, “On the security of ownership watermarking of digital images based on SVD decomposition,” J. Electron. Imaging 19, 013007 (2010).
  • 31. H.−C. Ling, R. C.−W. Phan, and S.−H. Heng, “On the security of a hybrid watermarking algorithm based on singular value decomposition and Radon transform,” Int. J. Electron. Commun. 65, 958–960 (2011).
  • 32. S. Rastegar, F. Namazi, K. Yaghmaie, and A. Aliabadian, “Hybrid watermarking algorithm based on singular value decomposition and radon transform,” Int. J. Electron. Commun. 65, 658–663 (2011).
  • 33. E. Beltrami, “Sulle funzioni bilineari,” Giornale de Matematiche 11, 98–106 (1873) (in Italian).
  • 34. C. Jordan, “Mémoire sur les formes trilinéaires,” J. Mathématiques Pures et Appliquées 19, 35–54 (1874) (in French).
  • 35. L. Autonne, “Sur les groupes linéaires, réels et orthogonaux,” Bulletin de la société mathématique de France, 1902 (in French).
  • 36. C. Eckart and G. Young, “A principal axis transformation for Non−Hermitian matrices,” Bulletin of the American Mathematical Society 45, 118–121 (1939).
  • 37. P. Waldemar and T. Ramstad, “Image compression using singular value decomposition with bit allocation and scalar quantization,” in Proc. Nordic Signal Process. Symp., 83–86, (1996).
  • 38. K. Chung, C. Shen, and L. Chang, “A novel SVD and VQ−based image hiding scheme,” Pattern Recogn. Lett. 22, 1051–1058 (2001).
  • 39. K. Konstantinides, B. Natarajan, and G. Yovanof, “Noise estimation and filtering using block−based singular value decomposition,” IEEE T. Image Process. 6, 479–483 (1997).
  • 40. P. Bao and X. Ma, “Image adaptive watermarking using wavelet domain singular value decomposition,” IEEE T. Circuits and Systems for Video Technology 15, 96–102 (2005).
  • 41. W. Sweldens, “The lifting scheme: A construction of second generation wavelets,” SIAM J. Mathematical Analysis 29, 511–546 (1997).
  • 42. I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps,” J. Fourier Analysis and Application 4, 247–269 (1998).
  • 43. J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in IEEE Int. Conf. Neural Networks 4, 1942–1948 (1995).
  • 44. R. Marler and J. Arora, “Survey of multi−objective optimization methods for engineering,” Structural and Multidisciplinary Optimization 26, 369–395 (2004).
  • 45. K. Loukhaoukha, “Comments on “A digital watermarking scheme based on singular value decomposition and tiny genetic algorithm”,” Digit. Signal Process., 23, 1334, (2013).
  • 46. C.−C. Lai, “A digital watermarking scheme based on singular value decomposition and tiny genetic algorithm”, Digit. Signal Process. 21, 522–527 (2011).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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