A DWT Domain High-Fidelity Anti-Tampering Digital Watermarking Algorithm for Medical Image
Abstract
In order to prevent medical images from being tampered in network transmission, a high fidelity anti-tampering medical image digital watermarking algorithm in DWT domain is proposed. The algorithm uses Logistic chaotic mapping to generate watermark embedding position sequence and watermark bit sequence with high ergodicity and randomness; Furthermore, the proposed DWT-SIN algorithm is used to embed and extract watermarks in DWT domain. The DWT-SIN algorithm slightly modifies each DWT coefficient corresponding to the embedding position sequence, so that the sine transforming value of the modified DWT coefficient is exactly equal to the corresponding item of the watermark bit sequence, thus achieving high fidelity for the watermarked image. Based on this, a chaotic key adaptive watermarking algorithm is proposed to achieve a given target fidelity for the watermarked image. The algorithm adaptively adjust the chaotic position key and then obtain the ideal watermark embedding position sequence, which effectively reduce the distortion of the watermarked medical image. In order to reduce the computational complexity of the algorithm, a chaotic key segmentation adaptive watermarking algorithm is further proposed. The experimental results show that the algorithm in this paper not only greatly improves the quality of the watermarked image, but also can locate the tampered position and present the tampering rate.
References
[1]
Raj, N.R.N., Shreelekshmi, R. A survey on fragile watermarking based image authentication schemes. Multimed Tools Appl 80, 19307–19333 (2021). https://doi.org/10.1007/s11042-021-10664-y
[2]
Fares, K., Khaldi, A., Redouane, K., & Salah, E. (2021). DCT & DWT based watermarking scheme for medical information security. Biomedical Signal Processing and Control, 66, 102403. https://doi.org/10.1016/j.bspc.2020.102403
[3]
Anand, A., & Singh, A. K. (2020). An improved DWT-SVD domain watermarking for medical information security. Computer Communications, 152, 72-80. https://doi.org/10.1016/j.comcom.2020.01.038
[4]
Zermi, N., Khaldi, A., Kafi, M. R., Kahlessenane, F., & Euschi, S. (2021). Robust SVD-based schemes for medical image watermarking. Microprocessors and Microsystems, 84, 104134. https://doi.org/10.1016/j.micpro.2021.104134
[5]
Kahlessenane, F., Khaldi, A., Kafi, R. A robust blind medical image watermarking approach for telemedicine applications. Cluster Comput 24, 2069–2082 (2021). https://doi.org/10.1007/s10586-020-03215-x
[6]
Thanki, R., Kothari, A. & Borra, S. Hybrid, blind and robust image watermarking: RDWT – NSCT based secure approach for telemedicine applications. Multimed Tools Appl 80, 27593–27613 (2021). https://doi.org/10.1007/s11042-021-11064-y
[7]
Su, GD., Chang, CC. & Chen, CC. A hybrid-Sudoku based fragile watermarking scheme for image tampering detection. Multimed Tools Appl 80, 12881–12903 (2021). https://doi.org/10.1007/s11042-020-10451-1
[8]
Bhalerao, S., Ansari, I.A. & Kumar, A. A secure image watermarking for tamper detection and localization. J Ambient Intell Human Comput 12, 1057–1068 (2021). https://doi.org/10.1007/s12652-020-02135-3
[9]
Hurrah, N.N., Parah, S.A. & Sheikh, J.A. Embedding in medical images: an efficient scheme for authentication and tamper localization. Multimed Tools Appl 79, 21441–21470 (2020). https://doi.org/10.1007/s11042-020-08988-2
[10]
Ravichandran, D., Praveenkumar, P., Rajagopalan, S. ROI-based medical image watermarking for accurate tamper detection, localisation and recovery. Med Biol Eng Comput 59, 1355–1372 (2021). https://doi.org/10.1007/s11517-021-02374-2
[11]
Alshanbari, H.S. Medical image watermarking for ownership & tamper detection. Multimed Tools Appl 80, 16549–16564 (2021). https://doi.org/10.1007/s11042-020-08814-9
[12]
Nazari, M., Mehrabian, M. A novel chaotic IWT-LSB blind watermarking approach with flexible capacity for secure transmission of authenticated medical images. Multimed Tools Appl 80, 10615–10655 (2021). https://doi.org/10.1007/s11042-020-10032-2
[13]
Soualmi, A., Alti, A. & Laouamer, L. A novel blind watermarking approach for medical image authentication using MinEigen value features. Multimed Tools Appl 80, 2279–2293 (2021). https://doi.org/10.1007/s11042-020-09614-x
[14]
Abdel-Nabi, H., & Al-Haj, A. (2020). Frequency domain based data hiding for encrypted medical images. In Intelligent Data Security Solutions for e-Health Applications (pp. 21-56). Academic Press. https://doi.org/10.1016/B978-0-12-819511-6.00002-9
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- A DWT Domain High-Fidelity Anti-Tampering Digital Watermarking Algorithm for Medical Image
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Published In
June 2023
293 pages
ISBN:9798400707919
DOI:10.1145/3608251
Copyright © 2023 ACM.
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Published: 17 August 2023
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ICCMS 2023
ICCMS 2023: 2023 The 15th International Conference on Computer Modeling and Simulation
June 16 - 18, 2023
Dalian, China
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