Block-Adaptive Chaotic Watermarking with Enhanced Tamper Localization for Medical Image Authentication

Raghda Abd Ul Rab Abd Ul Hasan

doi:10.58564/IJSER.5.2.2026.367

Authors

Raghda Abd Ul Rab Abd Ul Hasan Electrical Engineering Department, College of Engineering, Al-Iraqia University, Iraq

DOI:

https://doi.org/10.58564/IJSER.5.2.2026.367

Keywords:

Medical image watermarking, Block-adaptive embedding, Chaotic encryption, Henon-Sine map, QR code

Abstract

Medical images have recently been exposed to unintentional and intentional destruction by unauthorized persons in the storage and transfer of medical images. This undermines the integrity and reliability of diagnostic information. Thus, the paper outlines a watermarking system to authenticate against medical image manipulation. The process involves the application of a sequential chaotic map to encode patient information on a QR code. A texture feature and entropy-based hybrid model are used to determine appropriate image regions to be used in embedding. The system also has an image manipulation detection and locating tool to maintain diagnostic content. X-ray, CT and MRI images were used for experimentation. The findings show reasonable visual quality and data recovery, and achieve high imperceptibility with PSNR value exceeding 56 dB across the different types of attacks, which proves that the proposed method is reliable in ensuring that medical images are not compromised to a range of attacks.

References

[1] M. E. H. Kahla, M. Beggas, A. Laoui, M. Hammoudeh, and L. Laouamer, "Blockchain-Watermarking scheme based K-Means for medical image," Transp. Res. Procedia, vol. 84, pp. 51–58, 2025, doi: 10.1016/j.trpro.2025.03.044. DOI: https://doi.org/10.1016/j.trpro.2025.03.044

[2] A. Benyoucef, A. Goudjil, M. Hamadouche, M. C. Boutalbi, M. Ammar, and M. E. H. Daho, "High-capacity DWT-SVD watermarking for MRI images embedding MITR medical information," Results Eng., vol. 27, p. 105795, 2025, doi: 10.1016/j.rineng.2025.105795. DOI: https://doi.org/10.1016/j.rineng.2025.105795

[3] A. Aminuddin, F. Ernawan, D. Nincarean, A. Amrullah, and D. Ariatmanto, "TCBR and TCBD: Evaluation metrics for tamper coincidence problem in fragile image watermarking," Eng. Sci. Technol. Int. J., vol. 56, p. 101790, 2024, doi: 10.1016/j.jestch.2024.101790. DOI: https://doi.org/10.1016/j.jestch.2024.101790

[4] P. Aberna and L. Agilandeeswari, "POWBWM: Proof of work consensus cryptographic blockchain-based adaptive watermarking system for tamper detection applications," Alex. Eng. J., vol. 112, pp. 510–537, 2025, doi: 10.1016/j.aej.2024.10.016. DOI: https://doi.org/10.1016/j.aej.2024.10.016

[5] P. W. Adi, A. Sugiharto, M. M. Hakim, D. R. I. M. Setiadi, and E. Winarno, "Efficient fragile watermarking for image tampering detection using adaptive matrix on chaotic sequencing," Intell. Syst. Appl., vol. 26, p. 200530, 2025, doi: 10.1016/j.iswa.2025.200530. DOI: https://doi.org/10.1016/j.iswa.2025.200530

[6] M. M. Darwish, A. A. Farhat, and T. M. El-Gindy, "Convolutional neural network and 2D logistic-adjusted-Chebyshev-based zero-watermarking of color images," Multimedia Tools Appl., vol. 83, pp. 29969–29995, 2024, doi: 10.1007/s11042-023-16649-3. DOI: https://doi.org/10.1007/s11042-023-16649-3

[7] M. A. M. El-Bendary, O. S. Faragallah, and S. S. Nassar, "An efficient hidden marking approach for forensic and contents verification of digital images," Multimedia Tools Appl., vol. 82, pp. 25527–25558, 2023, doi: 10.1007/s11042-022-14104-3. DOI: https://doi.org/10.1007/s11042-022-14104-3

[8] T. B. Taha, "A review of effective image watermarking methods with future directions," in *Proc. 2025 IEEE 22nd Int. Multi-Conf. on Systems, Signals & Devices (SSD)*, 2025, doi: 10.1109/SSD64182.2025.10989860. DOI: https://doi.org/10.1109/SSD64182.2025.10989860

[9] H. Mareen, L. Antohougov, G. Van Wallendael, and P. Lambert, "Blind Deep-Learning-Based Image Watermarking Robust Against Geometric Transformations," Ghent University - imec, IDLab, Dept. of Electron. and Inf. Syst., Ghent, Belgium, 2024. [Online]. Available: [PDF: 9.pdf] DOI: https://doi.org/10.1109/ICCE59016.2024.10444317

[10] S. Tang, J. Ni, W. Su, and Y. Zhang, "DWW: Robust Deep Wavelet-Domain Watermarking With Enhanced Frequency Mask," IEEE Signal Process. Lett., vol. 31, pp. 3074–3078, 2024, doi: 10.1109/LSP.2024.3490399. DOI: https://doi.org/10.1109/LSP.2024.3490399

[11] S. Boujerfaoui, A. Mançour-Billah, H. Douzi, and R. Harba, "GeoViT: Mixed-Scale Transformer for Perspective Correction in Print-Cam Image Watermarking," IEEE Access, vol. 13, pp. 96503–96515, 2025, doi: 10.1109/ACCESS.2025.3575472. DOI: https://doi.org/10.1109/ACCESS.2025.3575472

[12] S. Trivedi, A. Dam, B. Chidirala, and B. Acharya, "Enhancing image watermarking efficiency through advanced deep learning: A novel approach with modified Res-Caption blocks and practical applications in modern scenarios," Digit. Signal Process., vol. 154, p. 104684, 2024, doi: 10.1016/j.dsp.2024.104684. DOI: https://doi.org/10.1016/j.dsp.2024.104684

[13] C. Qin, S. Gao, X. Zhang, and G. Feng, "CADW: CGAN-Based Attack on Deep Robust Image Watermarking," IEEE MultiMedia, vol. 30, no. 1, pp. 28–36, Jan.–Mar. 2023, doi: 10.1109/MMUL.2022.3213004. DOI: https://doi.org/10.1109/MMUL.2022.3213004

[14] B. Yin and K. Yin, "Robust Image Watermarking Using Bidirection-Interactive and Context-Aware Networks," IEEE Trans. Circuits Syst. Video Technol., early access, 2025, doi: 10.1109/TCSVT.2025.3543989. DOI: https://doi.org/10.1109/TCSVT.2025.3543969

[15] S. Wu, W. Lu, and X. Luo, "Robust watermarking based on multi-layer watermark feature fusion," IEEE Trans. Multimedia, early access, 2025, doi: 10.1109/TMM.2025.3643079. DOI: https://doi.org/10.1109/TMM.2025.3543079

[16] F. Bianchi and T.-C. Dinh, "Every complex Hénon map is exponentially mixing of all orders and satisfies the CLT," Forum Math. Sigma, vol. 12, no. e4, pp. 1–12, 2024, doi: 10.1017/fms.2023.110. DOI: https://doi.org/10.1017/fms.2023.110

[17] J. H. Awad, "Enhancing Robustness and Imperceptibility with a Texture-Based Adaptive QIM Approach," Iraqi J. Sci., vol. 65, no. 10, pp. 5837–5848, 2024, doi: 10.24996/ijs.2024.65.10.40. DOI: https://doi.org/10.24996/ijs.2024.65.10.40

[18] Y. Liu, C. Shi, X. Li, X. Niu, and Y. Liu, "A Robust Blind Image Watermarking using GLCM-Based Block Selection and Repetition-Coded RDWT-SVD Embedding," Preprint, pp. 1–18, 2024. [Online]. Available: SSRN 5192625 DOI: https://doi.org/10.2139/ssrn.5192625

[19] T. Pavithra and N. Sathisha, "Texture-Based Biomedical Anomaly Detection Using Supervised Learning Techniques and GLCM," J. Inf. Syst. Eng. Manag., vol. 9, no. 48, pp. 998–1016, 2024. [Online]. Available: https://www.jisem-journal.com/

[20] S. He, B. Yan, X. Wu, H. Wang, M. Wang, and H. H. C. Iu, "Spatiotemporal Chaos in a Sine Map Lattice With Discrete Memristor Coupling," IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 71, no. 3, pp. 1039–1049, Mar. 2024, doi: 10.1109/TCSI.2023.3347411. DOI: https://doi.org/10.1109/TCSI.2023.3347411