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A Novel Watermarking Technology Based on Posterior Probability SVM and Improved GA

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Cloud Computing and Security (ICCCS 2018)

Abstract

The widespread distribution of multimedia data cause copyright problems for digital content. This study makes use of digital image watermarking technology to protect copyright information, and proposes a scheme utilizes the support vector machine (SVM) based on posterior probability and the optimized genetic algorithm (GA). Firstly, each training image is divided into sub-blocks of 8 * 8 pixels, and they are trained and classified by the SVM to obtain the adaptive embedding strength. Secondly, after the operation of reproduction, crossover, mutation, the genetic algorithm generates new individuals in the search space by selection and recombination operators to optimize the objective function, and find out the best embedding position of the watermark. The 8 * 8 pixel sub-blocks were transformed by DCT when embedding. Finally, the watermark is extracted according to the embedding rules. Compared with the experimental results of other algorithms, the proposed scheme has better resistance against some common attacks, such as Histogram Equalization, Guassian Noise (0.04), Guassian Noise (0.05), JPEG (QF = 50), Salt-pepper Noise (0.01).

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References

  1. Chang, C.S., Shen, J.J.: Features classification forest: a novel development that is adaptable to robust blind watermarking techniques. IEEE Trans. Image Process. PP(99), 1 (2017)

    MathSciNet  Google Scholar 

  2. Aslantas, V.: An optimal robust digital image watermarking based on SVD using differential evolution algorithm. Optics Commun. 282(5), 769–777 (2009)

    Article  Google Scholar 

  3. Bhatnagar, G.: A new facet in robust digital watermarking framework. AEUE – Int. J. Electron. Commun. 66(4), 275–285 (2012)

    Article  Google Scholar 

  4. Cox, I.J., Miller, M.L., Bloom, J.A., et al.: Index - digital watermarking and steganography. In: Digital Watermarking & Steganography, 2nd edn., pp. 183–212 (2007)

    Google Scholar 

  5. Yen, C.T., Huang, Y.J.: Frequency domain digital watermark recognition using image code sequences with a back-propagation neural network. Multimedia Tools Appl. 16, 1–11 (2015)

    Google Scholar 

  6. Liu, Q., Jiang, X.: Design and realization of a meaningful digital watermarking algorithm based on RBF neural network. In: The Sixth World Congress on Intelligent Control and Automation, WCICA 2006, pp. 214–218. IEEE (2006)

    Google Scholar 

  7. Huynh-The, T., Hua, C.H., Tu, N.A., et al.: Selective bit embedding scheme for robust blind color image watermarking. Inf. Sci. 426, 1–18 (2018)

    Article  Google Scholar 

  8. Arsalan, M., Qureshi, A.S., Khan, A., et al.: Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique. Appl. Soft Comput. 51, 168–179 (2017)

    Article  Google Scholar 

  9. Maity, S.P., Maity, S., Sil, J., et al.: Perceptually adaptive MC-SS image watermarking using GA-NN hybridization in fading gain. Eng. Appl. Artif. Intell. 31(5), 3–14 (2014)

    Article  Google Scholar 

  10. Agarwal, C., Mishra, A., Sharma, A.: Gray-scale image watermarking using GA-BPN hybrid network. J. Vis. Commun. Image Representation 24(7), 1135–1146 (2013)

    Article  Google Scholar 

  11. Jawad, K., Khan, A.: Genetic algorithm and difference expansion based reversible watermarking for relational databases. J. Syst. Softw. 86(11), 2742–2753 (2013)

    Article  Google Scholar 

  12. Vapnik, V.: The Nature of Statistical Learning Theory. In: Conference on Artificial Intelligence, pp. 988–999. Springer, Heidelberg (1995). https://doi.org/10.1007/978-1-4757-3264-1

  13. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: The Workshop on Computational Learning Theory, pp. 144–152 (1992)

    Google Scholar 

  14. Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Q. Rev. Biol. 6(2), 126–137 (1992)

    Google Scholar 

  15. Liu, Y., Zhou, L.: Digital image embedding and extracting method based on wavelet transform domain. J. Shenyang Univ. Technol. 21, 1–6 (2018)

    Google Scholar 

  16. Jiang, X.D., Fan, H.Y., Lu, Z.M.: Blind robust watermarking algorithm based on logistic chaotic mapping and IWT-SVD quantization. Transducer Microsyst. Technol. 37(02), 131–135 (2018)

    Google Scholar 

  17. Zhou, Y., Jin, W.: A robust digital image multi-watermarking scheme in the DWT domain. In: International Conference on Systems and Informatics, Cairo, pp. 1851–1854 (2012)

    Google Scholar 

  18. Huynh-The, T., Hua, C.H., Tu, N.A., et al.: Selective bit embedding scheme for robust blind color image watermarking. Inf. Sci. 426, 1–18 (2018)

    Article  Google Scholar 

  19. Mishra, A., Rajpal, A., Bala, R.: Bi-directional extreme learning machine for semi-blind watermarking of compressed images. J. Inf. Secur. Appl. 38, 71–84 (2018)

    Google Scholar 

  20. Cha, B.H., Kuo, C.C.J.: Robust MC-CDMA-based fingerprinting against time-varying collusion attacks. IEEE Trans. Inf. Forensics Secur. 4(3), 302–317 (2009)

    Article  Google Scholar 

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Acknowledgement

The research was supported by Hainan Provincial Technology Project (Key Research and Development Project, Grant No. ZDYF2017171), Hainan Provincial Natural Science Foundation (Grant No. 117063 and No. 617079) and State Key Laboratory of Marine Resource Utilization in South China Sea.

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Correspondence to Xiaoyi Zhou .

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Liu, S., Zhao, M., Ma, J., Yao, J., Duan, Y., Zhou, X. (2018). A Novel Watermarking Technology Based on Posterior Probability SVM and Improved GA. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11066. Springer, Cham. https://doi.org/10.1007/978-3-030-00015-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-00015-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00014-1

  • Online ISBN: 978-3-030-00015-8

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