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Dual hybrid medical watermarking using walsh-slantlet transform

  • Roopam BamalEmail author
  • Singara Singh Kasana
Article
  • 54 Downloads

Abstract

A hybrid robust lossless data hiding algorithm is proposed in this paper by using the Singular Value Decomposition (SVD) with Fast Walsh Transform (FWT) and Slantlet Transform (SLT) for image authentication. These transforms possess good energy compaction with distinct filtering, which leads to higher embedding capacity from 1.8 bit per pixel (bpp) up to 7.5bpp. In the proposed algorithm, Artificial Neural Network (ANN) is applied for region of interest (ROI) detection and two different watermarks are created. Embedding is done after applying FWH by changing the SVD coefficients and by changing the highest coefficients of SLT subbands. In dual hybrid embedding first watermark is the ROI and another watermark consists of three parts, i.e., patients’ personal details, unique biometric ID and the key for encryption. Comparison of the proposed algorithm is done with the existing watermarking techniques for analyzing the performance. Experiments are simulated on the proposed algorithm by casting numerous attacks for testing the visibility, robustness, security, authenticity, integrity and reversibility. The resultant outcome proves that the watermarked image has an improved imperceptibility with a high level of payload, low time complexity and high Peak Signal to Noise Ratio (PSNR) against the existing approaches.

Keywords

Watermarking Slantlet SIM PSNR ANN AES 

Notes

References

  1. 1.
    Acharya R, Bhat PS, Kumar S, Min LC (2003) Transmission and storage of medical images with patient information. Comput Biol Med 33(4):303–310CrossRefGoogle Scholar
  2. 2.
    Acharya R, Niranjan UC, Iyengar SS, Kannathal N, Min LC (2004) Simultaneous storage of patient information with medical images in the frequency domain. Comput Methods Prog Biomed 76(1):13–19CrossRefGoogle Scholar
  3. 3.
    Alattar AM (2004) Reversible watermark using the difference expansion of a generalized integer transform. IEEE transactions on image processing 13(8):1147–1156MathSciNetCrossRefGoogle Scholar
  4. 4.
    Alvarez G, Li S, Hernandez L (2007) Analysis of security problems in a medical image encryption system. Comput Biol Med 37(3):424–427CrossRefGoogle Scholar
  5. 5.
    Arsalan M, Malik SA, Khan A (2012) Intelligent reversible watermarking in integer wavelet domain for medical images. J Syst Softw 85(4):883–894CrossRefGoogle Scholar
  6. 6.
    Bamal R, Kasana SS (2017) Slantlet based hybrid watermarking technique for medical images. Multimedia Tools and Applications 77:1–26Google Scholar
  7. 7.
    Bhatnagar G, Raman B (2009) Robust watermarking in multiresolution walsh-hadamard transform. In: IEEE international on advance computing conference 2009. IACC 2009. IEEE, pp 894–899Google Scholar
  8. 8.
    Biryukov A, Dunkelman O, Keller N, Khovratovich D, Shamir A (2010) Key recovery attacks of practical complexity on aes-256 variants with up to 10 rounds. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 299–319Google Scholar
  9. 9.
    Biryukov A, Khovratovich D, Nikolić I (2009) Distinguisher and related-key attack on the full aes-256. In: Advances in cryptology-CRYPTO 2009. Springer, pp 231–249Google Scholar
  10. 10.
    Cox IJ, Kilian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687CrossRefGoogle Scholar
  11. 11.
    da Silva PHF, Cruz RMS, Assuncao AGD (2010) Neuromodeling and natural optimization of nonlinear devices and circuits. System and Circuit Design for Biologically-Inspired Intelligent Learning, pp 1969067189Google Scholar
  12. 12.
    Fakhari P, Vahedi E, Lucas C (2011) Protecting patient privacy from unauthorized release of medical images using a bio-inspired wavelet-based watermarking approach. Digit Signal Process 21(3):433–446CrossRefGoogle Scholar
  13. 13.
    Garcia-Hernandez JJ, Gomez-Flores W, Rubio-Loyola J (2016) Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging. Comput Biol Med 68:37–48CrossRefGoogle Scholar
  14. 14.
    Hykin S (1999) Neural networks: a comprehensive foundation. Printice-hall. Inc., New JerseyGoogle Scholar
  15. 15.
    Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Glob Optim 39(3):459–471MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lei B, Tan E-L, Chen S, Ni D, Wang T, Lei H (2014) Reversible watermarking scheme for medical image based on differential evolution. Expert Syst Appl 41(7):3178–3188CrossRefGoogle Scholar
  17. 17.
    Li M, Poovendran R, Narayanan S (2005) Protecting patient privacy against unauthorized release of medical images in a group communication environment. Comput Med Imaging Graph 29(5):367–383CrossRefGoogle Scholar
  18. 18.
    Naheed T, Usman I, Khan TM, Dar AH, Shafique MF (2014) Intelligent reversible watermarking technique in medical images using ga and pso. Optik-Int J Light Electron Opt 125(11):2515–2525CrossRefGoogle Scholar
  19. 19.
    Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386CrossRefGoogle Scholar
  20. 20.
    Selesnick IW (1999) The slantlet transform. IEEE Trans Signal Process 47 (5):1304–1313MathSciNetCrossRefGoogle Scholar
  21. 21.
    Shih FY, Wu Y-T (2005) Robust watermarking and compression for medical images based on genetic algorithms. Inf Sci 175(3):200–216MathSciNetCrossRefGoogle Scholar
  22. 22.
    Shih FY, Zhong X (2016) High-capacity multiple regions of interest watermarking for medical images. Inf Sci 367:648–659CrossRefGoogle Scholar
  23. 23.
    Thodi DM, Rodríguez JJ (2007) Expansion embedding techniques for reversible watermarking. IEEE Trans Image Process 16(3):721–730MathSciNetCrossRefGoogle Scholar
  24. 24.
    Tian J (2003) Reversible data embedding using a difference expansion. IEEE Trans Circ Syst Video Technol 13(8):890–896CrossRefGoogle Scholar
  25. 25.
    Tian Y, Tan T, Wang Y, Fang Y (2003) Do singular values contain adequate information for face recognition? Pattern Recogn 36(3):649–655CrossRefGoogle Scholar
  26. 26.
    Wakatani A (2002) Digital watermarking for roi medical images by using compressed signature image. In: Proceedings of the 35th annual Hawaii international conference on system sciences 2002. HICSS. IEEE, pp 2043–2048Google Scholar
  27. 27.
    Wang Z-H, Lee C-F, Chang C-Y (2013) Histogram-shifting-imitated reversible data hiding. J Syst Softw 86(2):315–323CrossRefGoogle Scholar
  28. 28.
    Wei JC, Kern GM (1989) Commonality analysis: a linear cell clustering algorithm for group technology. Int J Prod Res 27(12):2053–2062CrossRefGoogle Scholar
  29. 29.
    Zain JM, Clarke M (2011) Reversible region of non-interest (roni) watermarking for authentication of dicom images. arXiv:1101.1603
  30. 30.
    Zhao Z, Luo H, Lu Z-M, Pan J-S (2011) Reversible data hiding based on multilevel histogram modification and sequential recovery. AEU-Int J Electron Commun 65(10):814–826CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar Institute of Engineering & TechnologyPatialaIndia

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