Efficient and Robust Medical Image Watermarking Based on Optimal Subband Tree Structuring and Discrete Fractional Fourier Transform

Anusha Chacko; Shanty Chacko

doi:10.24423/cames.547

Anusha Chacko Karunya Institute of Technology and Science, Coimbatore, and Vimal Jyothi Engineering College, Chemperi
Shanty Chacko Karunya Institute of Technology and Science, Coimbatore

DOI: http://dx.doi.org/10.24423/cames.547

Abstract

In order to solve the security problems associated with medical information and improve the robustness of watermarking algorithms for medical images, a unique approach to watermarking based on block operations is presented. This study considers the medical images as the cover image, with the watermark logo considered secret information that needs to be protected over the wireless transmission in telemedicine. In the embedding phase, input with the discrete fractional Fourier transform is first applied to the input, and then level 2 wavelet decomposition is carried out to determine the optimal sub-band tree. For each tree node on level 2, the approximated and detailed coefficient is determined through the feature analysis perspective. The novelty of the adopted methodology is its simplified transformation and embedding process. Upon receiving a complex matrix, it separates the real part from imaginary part where block transformation is carried out for embedding the watermark pixels. In the extraction phase, just a reverse operation is performed. The watermarking evaluation is performed by simulating various image processing attacks on watermarked medical images. The simulation outcome demonstrates the effectiveness of that proposed watermarking scheme against various attacks. The proposed watermarking technique is robust under various attacks based on image statistics such as PSNR, BER, and the correlation coefficient.

Keywords

medical image, watermarking, discrete FFT, decomposition, security,

References

1. B. Al Hayani, H. Ilhan, Image transmission over decode and forward based cooperative wireless multimedia sensor networks for Rayleigh fading channels in medical internet of things (MIoT) for remote healthcare and health communication monitoring, Journal of Medical Imaging And Health Informatics, 10(1): 160–168, 2020, doi: 10.1166/jmihi.2020.2691.
2. R.F. Mansour, E.M. Abdelrahim, An evolutionary computing enriched RS attack resilient medical image steganography model for telemedicine applications, Multi-dimensional Systems and Signal Processing, 30(2): 791–814, 2019, doi: 10.1007/s11045-018-0575-3.
3. J. Zain, M. Clarke, Security in telemedicine: Issues in watermarking medical images, [in:] 3rd International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, March 27–31, Tunisia, 2005.
4. R. Thanki, S. Borra, Medical Imaging and Its Security in Telemedicine Applications, Cham, Switzerland, Springer, 2019, doi: 10.1007/978-3-319-93311-5.
5. F. Cao, H.K. Huang, X.Q. Zhou, Medical image security in a HIPAA mandated PACS environment, Computerized Medical Imaging and Graphics, 27(2–3): 185–196, 2003, doi: 10.1016/s0895-6111(02)00073-3.
6. S. Rai, R. Boghey, D. Shahane, P. Saxena, Digital image watermarking against geometrical attack, [in:] R.K. Shukla, J. Agrawal, S. Sharma, G. Singh Tomer [Eds.], Data, Engineering and Applications, pp. 129–145, Springer, Singapore, 2019, doi: 10.1007/978-981-13-6351-1_12.
7. M. Begum, M.S. Uddin, Implementation of secured and robust DFT-based image watermark through hybridization with decomposition algorithm, SN Computer Science, 2(3): 221, 2021, doi: 10.1007/s42979-021-00608-6.
8. S. Kumar, B.K. Singh, DWT based color image watermarking using maximum entropy, Multimedia Tools and Application, 80(10): 15487–15510, 2021, doi: 10.1007/s11042-020-10322-9.
9. D. Singh, S.K. Singh, DCT based efficient fragile watermarking scheme for image authentication and restoration, Multimedia Tools and Application, 76(1): 953–977, 2017, doi: 10.1007/s11042-015-3010-x.
10. R. Sinhal, S. Sharma, I.A. Ansari, V. Bajaj, Multipurpose medical image watermarking for effective security solutions, Multimedia Tools and Applications, 81(10): 14045–14063, 2022, doi: 10.1007/s11042-022-12082-0.
11. I.K. Yeo, H.J. Kim, Generalized patchwork algorithm for image watermarking, Multimedia Systems, 9(3): 261–265, 2003, doi: 10.1007/s00530-003-0097-0.
12. F. Zhang, T. Luo, G. Jiang, M. Ju, H. Xu, W. Zhou, A novel robust color image watermarking method using RGB correlations, Multimedia Tools and Applications, 78(14): 20133–20155, 2019, doi: 10.1007/s11042-019-7326-9.
13. M. Nazari, M. Mehrabian, A novel chaotic IWT-LSB blind watermarking approach with flexible capacity for secure transmission of authenticated medical images, Multimedia Tools and Applications, 80(7): 10615–10655, 2021, doi: 10.1007/s11042-020-10032-2.
14. P. Priyadarshini, A. Dash, K. Naik, Secure sharing of medical images using watermarking technique, [in:] A.K. Das, J. Nayak, B. Naik, S. Vimal, D. Pelusi [Eds.], Computational Intelligence in Pattern Recognition, CIPR 2022, Lecture Notes in Networks and Systems, Vol. 480, pp. 592–604, Springer, Singapore, 2022, doi: 10.1007/978-981-19-3089-8_56.
15. A.K. Singh, B. Kumar, G. Singh, A. Mohan, Medical image watermarking techniques: a technical survey and potential challenges, [in:] Medical Image Watermarking, pp. 13–41, Springer, Cham, 2017, doi: 10.1007/978-3-319-57699-2_2.
16. P. Parashar, R.K. Singh, A survey: digital image watermarking techniques, International Journal of Signal Processing, Image Processing and Pattern Recognition, 7(6): 111–124, 2014, doi: 10.14257/ijsip.2014.7.6.10.
17. A.K. Singh, N. Sharma, M. Dave, A. Mohan, A novel technique for digital image watermarking in spatial domain, [in:] 2021 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, pp. 497–501, Solan, India, 2012, doi: 10.1109/PDGC.2012.6449871.
18. H.J. Ko, C.T. Huang, G. Horng, S.J.Wang, Robust and blind image watermarking in DCT domain using inter-block coefficient correlation. Information Sciences, 517(1): 128–147, 2020, doi: 10.1016/j.ins.2019.11.005.
19. A. Ray, S. Roy, Recent trends in image watermarking techniques for copyright protection: a survey, International Journal of Multimedia Information Retrieval, 9(4): 249–270, 2020, doi: 10.1007/s13735-020-00197-9.
20. A. Anand, A.K. Singh, Watermarking techniques for medical data authentication: a survey, Multimedia Tools and Applications, 80(20): 30165–30197, 2021, doi: 10.1007/s11042-020-08801-0.
21. B. Hassan, R. Ahmed, B. Li, O. Hassan, An imperceptible medical image watermarking framework for automated diagnosis of retinal pathologies in an eHealth arrangement, IEEE Access, 7: 69758–69775, 2019, doi: 10.1109/ACCESS.2019.2919381.
22. D. Nuñez-Ramirez, M. Cedillo-Hernandez, M. Nakano-Miyatake, H. Perez-Meana, Efficient management of ultrasound images using digital watermarking, IEEE Latin America Transactions, 18(8): 1398–1406, 2020, doi: 10.1109/TLA.2020.9111675.
23. K.M. Hosny, M.M. Darwish, K. Li, A. Salah, Parallel multi-core CPU and GPU for fast and robust medical image watermarking, IEEE Access, 6: 77212–77225, 2018, doi: 10.1109/ACCESS.2018.2879919.
24. G.-D. Su, C.-C. Chang, C.-C. Lin, Effective self-recovery and tampering localization fragile watermarking for medical images, IEEE Access, 8: 160840–160857, 2020, doi: 10.1109/ACCESS.2020.3019832.
25. X. Liu et al., A novel robust reversible watermarking scheme for protecting authenticity and integrity of medical images, IEEE Access, 7: 76580–76598, 2019, doi: 10.1109/ACCESS.2019.2921894.
26. S. Haddad, G. Coatrieux, A. Moreau-Gaudry, M. Cozic, Joint watermarking-encryption-JPEG-LS for medical image reliability control in encrypted and compressed domains, IEEE Transactions on Information Forensics and Security, 15: 2556–2569, 2020, doi: 10.1109/TIFS.2020.2972159.
27. A. Shehab et al., Secure and robust fragile watermarking scheme for medical images, IEEE Access, 6: 10269–10278, 2018, doi: 10.1109/ACCESS.2018.2799240.
28. J. Liu, J. Ma, J. Li, M. Huang, N. Sadiq, Y. Ai, Robust watermarking algorithm for medical volume data in internet of medical things, IEEE Access, 8: 93939–93961, 2020, doi: 10.1109/ACCESS.2020.2995015.
29. A.A. Abd El-Latif, B. Abd-El-Atty, M.S. Hossain, M.A. Rahman, A. Alamri, B.B. Gupta, Efficient quantum information hiding for remote medical image sharing, IEEE Access, 6: 21075–21083, 2018, doi: 10.1109/ACCESS.2018.2820603.
30. N.A. Memon, A. Alzahrani, Prediction-based reversible watermarking of CT scan images for content authentication and copyright protection, IEEE Access, 8: 75448–75462, 2020, doi: 10.1109/ACCESS.2020.2989175.
31. N.A. Loan, N.N. Hurrah, S.A. Parah, J.W. Lee, J.A. Sheikh, G.M. Bhat, Secure and robust digital image watermarking using coefficient differencing and chaotic encryption, IEEE Access, 6: 19876–19897, 2018, doi: 10.1109/ACCESS.2018.2808172.
32. K. Gourrame, F. Ros, H. Douzi, R. Harba, R. Riad, Fourier image watermarking: Printcam application, Electronics, 11(2): 266, 2022, doi: 10.3390/electronics11020266.
33. J. Arif, S.P. Gangwar, An efficient watermarking process based on three-level DWT and FFT technique, [in:] D. Harvey, H. Kar, S. Verma, V. Bhadauria [Eds.], Advances in VLSI, Communication, and Signal Processing, Vol. 683, Springer, pp. 303–311, 2020, doi: 10.1007/978-981-15-6840-4_24.
34. R. Kumari, A. Mustafi, An optimized framework for digital watermarking based on multi-parameterized 2D-FrFT using PSO, Optik, 248: 168077, 2021, doi: 10.1016/j.ijleo.2021.168077.
35. N. Hasan, M.S. Islam, W. Chen, M.A. Kabir, S. Al-Ahmadi, Encryption based image watermarking algorithm in 2DWT-DCT domains, Sensors, 21(16): 5540, 2021, doi: 10.3390/s21165540.
36. A. Dwivedi, M. Yadav, A. Kumar, FFT-based zero-bit watermarking for facial recognition and its security, [in:] M. Dave, R. Garg, M. Dua, J. Hussien [Eds.], Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences: Algorithms for Intelligent Systems, Vol. 195, pp. 403–417, Springer, Singapore, 2021, doi: 10.1007/978-981-15-7533-4_31.
37. N. Chakrabarty, Brain MRI Images for Brain Tumor Detection, Kaggle, 2019, https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection.
38. A. Das, Discrete Fourier transform, [in:] Signal Conditioning, Signals and Communication Technology, Springer, Berlin, Heidelberg, pp. 159–192, 2012, doi: 10.1007/978-3-642-28818-0_7.
39. J. Evers, F. Evers, F. Goppelt, R. Schmidt-Vollus, Singular spectrum analysis-based image sub-band decomposition filter banks, EURASIP Journal on Advances in Signal Processing, 2020: 29, 2020, doi: 10.1186/s13634-020-00685-4.