Efficient and Robust Medical Image Watermarking Based on Optimal Subband Tree Structuring and Discrete Fractional Fourier Transform
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, securityReferences
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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, https://doi.org/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, https://doi.org/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, https://doi.org/10.1016/s0895-6111%2802%2900073-3
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, https://doi.org/10.1007/978-981-13-6351-1_12
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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, https://doi.org/10.1007/s11042-015-3010-x
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11. I.K. Yeo, H.J. Kim, Generalized patchwork algorithm for image watermarking, Multimedia Systems, 9(3): 261–265, 2003, https://doi.org/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, https://doi.org/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, https://doi.org/10.1007/s11042-020-10032-2
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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, https://doi.org/10.14257/ijsip.2014.7.6.10
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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, https://doi.org/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, https://doi.org/10.1007/s13735-020-00197-9
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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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/10.1186/s13634-020-00685-4
