Skip to main content
SpringerLink
Log in

Dual hybrid medical watermarking using walsh-slantlet transform

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Acharya R, Bhat PS, Kumar S, Min LC (2003) Transmission and storage of medical images with patient information. Comput Biol Med 33(4):303–310

    Article  Google Scholar 

  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):13–19

    Article  Google Scholar 

  3. Alattar AM (2004) Reversible watermark using the difference expansion of a generalized integer transform. IEEE transactions on image processing 13(8):1147–1156

    Article  MathSciNet  Google Scholar 

  4. Alvarez G, Li S, Hernandez L (2007) Analysis of security problems in a medical image encryption system. Comput Biol Med 37(3):424–427

    Article  Google Scholar 

  5. Arsalan M, Malik SA, Khan A (2012) Intelligent reversible watermarking in integer wavelet domain for medical images. J Syst Softw 85(4):883–894

    Article  Google Scholar 

  6. Bamal R, Kasana SS (2017) Slantlet based hybrid watermarking technique for medical images. Multimedia Tools and Applications 77:1–26

  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 894–899

  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 299–319

  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 231–249

  10. Cox IJ, Kilian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687

    Article  Google Scholar 

  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 1969067189

  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):433–446

    Article  Google Scholar 

  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 68:37–48

    Article  Google Scholar 

  14. Hykin S (1999) Neural networks: a comprehensive foundation. Printice-hall. Inc., New Jersey

    Google Scholar 

  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):459–471

    Article  MathSciNet  MATH  Google Scholar 

  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):3178–3188

    Article  Google Scholar 

  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):367–383

    Article  Google Scholar 

  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):2515–2525

    Article  Google Scholar 

  19. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386

    Article  Google Scholar 

  20. Selesnick IW (1999) The slantlet transform. IEEE Trans Signal Process 47 (5):1304–1313

    Article  MathSciNet  MATH  Google Scholar 

  21. Shih FY, Wu Y-T (2005) Robust watermarking and compression for medical images based on genetic algorithms. Inf Sci 175(3):200–216

    Article  MathSciNet  Google Scholar 

  22. Shih FY, Zhong X (2016) High-capacity multiple regions of interest watermarking for medical images. Inf Sci 367:648–659

    Article  Google Scholar 

  23. Thodi DM, Rodríguez JJ (2007) Expansion embedding techniques for reversible watermarking. IEEE Trans Image Process 16(3):721–730

    Article  MathSciNet  Google Scholar 

  24. Tian J (2003) Reversible data embedding using a difference expansion. IEEE Trans Circ Syst Video Technol 13(8):890–896

    Article  Google Scholar 

  25. Tian Y, Tan T, Wang Y, Fang Y (2003) Do singular values contain adequate information for face recognition? Pattern Recogn 36(3):649–655

    Article  Google Scholar 

  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 2043–2048

  27. Wang Z-H, Lee C-F, Chang C-Y (2013) Histogram-shifting-imitated reversible data hiding. J Syst Softw 86(2):315–323

    Article  Google Scholar 

  28. Wei JC, Kern GM (1989) Commonality analysis: a linear cell clustering algorithm for group technology. Int J Prod Res 27(12):2053–2062

    Article  Google Scholar 

  29. Zain JM, Clarke M (2011) Reversible region of non-interest (roni) watermarking for authentication of dicom images. arXiv:1101.1603

  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):814–826

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, 147004, India

    Roopam Bamal & Singara Singh Kasana

Authors
  1. Roopam Bamal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Singara Singh Kasana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roopam Bamal.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bamal, R., Kasana, S.S. Dual hybrid medical watermarking using walsh-slantlet transform. Multimed Tools Appl 78, 17899–17927 (2019). https://doi.org/10.1007/s11042-018-6820-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6820-9

Keywords

Search

Navigation