High Authentication MRI Image in Digital Watermarking and Stenography Technique
Keywords:
MRI Image Security, Digital Watermarking, DWT–SVD, MLSB Steganography, Medical Image Authentication, Data Integrity, Information Hiding, Telemedicine SecurityAbstract
Magnetic Resonance Imaging (MRI) is a critical diagnostic tool in modern healthcare systems, where image authenticity, integrity, and patient data confidentiality are of paramount importance. During digital transmission and storage, MRI images are vulnerable to unauthorized modification, data tampering, and privacy breaches, which may lead to incorrect clinical decisions. To address these challenges, this paper presents a high-authentication MRI image security framework using a hybrid approach that integrates digital watermarking and steganography techniques. Robust authentication is achieved through digital watermarking based on the Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD), which embeds authentication information such as encrypted patient identity or hash values into selected frequency sub-bands of the MRI image. The DWT provides multi-resolution analysis, while SVD ensures stability and robustness against common image processing attacks.
In addition, Modified Least Significant Bit (MLSB) steganography is employed to securely conceal sensitive patient information within the MRI image without affecting diagnostic quality. The MLSB technique enhances payload capacity and security compared to traditional LSB methods while maintaining high imperceptibility. The combined DWT–SVD watermarking and MLSB steganography framework ensures robust authentication, data integrity verification, and confidentiality of medical information. Performance evaluation using metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Computation time reversibility demonstrates that the proposed approach preserves high visual quality and diagnostic reliability. The proposed framework is well suited for secure MRI image transmission in telemedicine, cloud-based healthcare, and hospital information systems.
References
Mahbuba Begum, Sumaita Binte Shorif, Mohammad Shorif Uddin, Jannatul Ferdush, Tony Jan, Alistair Barros, and Md. Whaiduzzaman, “Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness,” MDPI, 2024.
Chao Qin, Xiaoyan Li, Zhen Zhang, Fang Li, Xiaoming Zhang, and Guang Feng, “Print-Camera Resistant Image Watermarking With Deep Noise Simulation and Constrained Learning,” IEEE Transactions on Multimedia, vol. 26, pp. 2164–2177, 2024.
Zhongliang Wang, Owen Byrnes, Hao Wang, Rui Sun, Chao Ma, Hao Chen, Qiang Wu, and Min Xue, “Data Hiding With Deep Learning: A Survey Unifying Digital Watermarking and Steganography,” IEEE Transactions on Computational Social Systems, vol. 10, no. 6, pp. 2985–2999, Dec. 2023.
Guanghui Ye, Junchao Gao, Bo Yin, Wen Xie, and Xianfeng Wei, “Deep Boosting Robustness of DNN-Based Image Watermarking via Dbmark,” in Proc. International Conference on Culture-Oriented Science and Technology (CoST), 2023, pp. 186–191.
Wenguang He, Zhanchuan Cai, and Yaomin Wang, “High-Fidelity Reversible Image Watermarking Based on Effective Prediction Error-Pairs Modification,” IEEE Transactions on Multimedia, 2020.
Nazir Ahmad Loan, Nasir Nazir Hurrah, Shabir Ahmad Parah, Jong Weon Lee, Javaid Ahmad Sheikh, and Ghulam Mohiuddin Bhat, “Secure and Robust Digital Image Watermarking Using Coefficient Differencing and Chaotic Encryption,” IEEE Access, 2018.
Mohammad Hussain, A. W. Abdul Wahab, N. Javed, and Ki-Hyun Jung, “Recursive Information Hiding Scheme Through LSB, PVD Shift, and MPE,” IETE Technical Review, vol. 35, no. 1, pp. 53–63, 2018.
Awdhesh Kumar Shukla, Akanksha Singh, Balvinder Singh, and Amod Kumar, “A Secure and High-Capacity Data-Hiding Method Using Compression, Encryption and Optimized Pixel Value Differencing,” IEEE Access, 2018.
Anindya Bose and S. P. Maity, “Spread Spectrum Image Watermark Detection on Degraded Compressed Sensing Measurements With Distortion Minimization,” Multimedia Tools and Applications, vol. 77, no. 16, pp. 20783–20808, 2018.
Baharak A., Fatih K., Jesus Martinez Del Rincon, and Ahmed B., “Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory,” IEEE Transactions on Computational Imaging, vol. 4, no. 1, pp. 46–59, 2018.
Etti Mathur and Manish Mathuria, “Unbreakable Digital Watermarking Using Combination of LSB and DCT,” in Proc. IEEE ICECA, 2017.
Aleksei Zhuvikin, “Selective Image Authentication Using Shearlet Coefficients Tolerant to JPEG Compression,” in Proc. IEEE Conference, pp. 681–688, 2017.
N. Senthil Kumaran and S. Abinaya, “Comparison Analysis of Digital Image Watermarking Using DWT and LSB Technique,” in Proc. ICCSP, IEEE, 2016.
Morteza Heidari, Nader Karimi, and Shadrokh Samavi, “A Hybrid DCT-SVD Based Image Watermarking Algorithm,” in Proc. Iranian Conference on Electrical Engineering (ICEE), IEEE, 2016.
Ajay Kumar Singh, Mayank Dave, and Anil Mohan, “Multilevel Encrypted Text Watermarking on Medical Images Using Spread-Spectrum in DWT Domain,” Wireless Personal Communications, vol. 83, no. 3, pp. 2133–2150, 2015.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
