Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography
Based on Hilbert Random Secure Distribution, a novel data-hiding method for embedding secret information about the patient in a cover image MRI sample has been proposed. Least significant bit (LSB) and most significant bit (MSB) techniques are applied for the physical hiding. Medical images confiden...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10287340/ |
| _version_ | 1797649792134807552 |
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| author | Hussein K. Alzubaidy Dhiah Al-Shammary Mohammed Hamzah Abed Ayman Ibaida Khandakar Ahmed |
| author_facet | Hussein K. Alzubaidy Dhiah Al-Shammary Mohammed Hamzah Abed Ayman Ibaida Khandakar Ahmed |
| author_sort | Hussein K. Alzubaidy |
| collection | DOAJ |
| description | Based on Hilbert Random Secure Distribution, a novel data-hiding method for embedding secret information about the patient in a cover image MRI sample has been proposed. Least significant bit (LSB) and most significant bit (MSB) techniques are applied for the physical hiding. Medical images confidentiality suffers from potential attacks and tracing by an unauthorized access. Technically, distributing the secret text in a random way on the cover image is the core security function of the proposed model. In order to evaluate the performance of the proposed solution, three quality metrics: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), percentage residual difference (PRD) and Structural Similarity Index measure (SSIM) were computed and compared on ten MRI images. Experimental results showed significant results in comparison with other models and reached average PSNR up to 61 db. Furthermore, the security analysis in case of <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> image samples show complex probability of distribution based on the Hilbert space model. |
| first_indexed | 2024-03-11T15:51:06Z |
| format | Article |
| id | doaj.art-912922202db444858a2c0965fdddfacf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T15:51:06Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-912922202db444858a2c0965fdddfacf2023-10-25T23:00:51ZengIEEEIEEE Access2169-35362023-01-011111581611582610.1109/ACCESS.2023.332575410287340Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy SteganographyHussein K. Alzubaidy0Dhiah Al-Shammary1Mohammed Hamzah Abed2https://orcid.org/0000-0003-4780-4252Ayman Ibaida3https://orcid.org/0000-0003-1581-7219Khandakar Ahmed4https://orcid.org/0000-0003-1043-2029Computer Science Department, University of Al-Qadisiyah, Al Diwaniyah, IraqComputer Science Department, University of Al-Qadisiyah, Al Diwaniyah, IraqComputer Science Department, University of Al-Qadisiyah, Al Diwaniyah, IraqIntelligent Technology Innovation Laboratory, Victoria University, Melbourne, VIC, AustraliaIntelligent Technology Innovation Laboratory, Victoria University, Melbourne, VIC, AustraliaBased on Hilbert Random Secure Distribution, a novel data-hiding method for embedding secret information about the patient in a cover image MRI sample has been proposed. Least significant bit (LSB) and most significant bit (MSB) techniques are applied for the physical hiding. Medical images confidentiality suffers from potential attacks and tracing by an unauthorized access. Technically, distributing the secret text in a random way on the cover image is the core security function of the proposed model. In order to evaluate the performance of the proposed solution, three quality metrics: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), percentage residual difference (PRD) and Structural Similarity Index measure (SSIM) were computed and compared on ten MRI images. Experimental results showed significant results in comparison with other models and reached average PSNR up to 61 db. Furthermore, the security analysis in case of <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> image samples show complex probability of distribution based on the Hilbert space model.https://ieeexplore.ieee.org/document/10287340/Patient privacysteganographyLSBMSBMRI samplesHilbert similarity |
| spellingShingle | Hussein K. Alzubaidy Dhiah Al-Shammary Mohammed Hamzah Abed Ayman Ibaida Khandakar Ahmed Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography IEEE Access Patient privacy steganography LSB MSB MRI samples Hilbert similarity |
| title | Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography |
| title_full | Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography |
| title_fullStr | Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography |
| title_full_unstemmed | Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography |
| title_short | Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography |
| title_sort | hilbert convex similarity for highly secure random distribution of patient privacy steganography |
| topic | Patient privacy steganography LSB MSB MRI samples Hilbert similarity |
| url | https://ieeexplore.ieee.org/document/10287340/ |
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