A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement
With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security mea...
| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
KeAi Communications Co., Ltd.
2024-01-01
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| Colecção: | International Journal of Cognitive Computing in Engineering |
| Assuntos: | |
| Acesso em linha: | http://www.sciencedirect.com/science/article/pii/S2666307424000305 |
| _version_ | 1826985743643312128 |
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| author | Mohamed Abdel Hameed M. Hassaballah Riem Abdelazim Aditya Kumar Sahu |
| author_facet | Mohamed Abdel Hameed M. Hassaballah Riem Abdelazim Aditya Kumar Sahu |
| author_sort | Mohamed Abdel Hameed |
| collection | DOAJ |
| description | With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image’s Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patient’s privacy and data integrity. |
| first_indexed | 2025-02-18T07:13:41Z |
| format | Article |
| id | doaj.art-ab2b504b51e64eb7910c9cae22fbd352 |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2025-02-18T07:13:41Z |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-ab2b504b51e64eb7910c9cae22fbd3522024-11-08T04:42:07ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742024-01-015379397A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacementMohamed Abdel Hameed0M. Hassaballah1Riem Abdelazim2Aditya Kumar Sahu3Department of Computer Science, Faculty of Computers and Information, Luxor University, Luxor, 85951, EgyptDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, AlKharj, 16278, Saudi Arabia; Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt; Corresponding author at: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, AlKharj, 16278, Saudi Arabia.Department of Information Systems, College of Information Technology, Misr University for Science and Technology, Giza, EgyptAmrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, IndiaWith recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image’s Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patient’s privacy and data integrity.http://www.sciencedirect.com/science/article/pii/S2666307424000305Medical informationMultimedia securityData hidingSteganographyLSBHOG-LSB |
| spellingShingle | Mohamed Abdel Hameed M. Hassaballah Riem Abdelazim Aditya Kumar Sahu A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement International Journal of Cognitive Computing in Engineering Medical information Multimedia security Data hiding Steganography LSB HOG-LSB |
| title | A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement |
| title_full | A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement |
| title_fullStr | A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement |
| title_full_unstemmed | A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement |
| title_short | A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement |
| title_sort | novel medical steganography technique based on adversarial neural cryptography and digital signature using least significant bit replacement |
| topic | Medical information Multimedia security Data hiding Steganography LSB HOG-LSB |
| url | http://www.sciencedirect.com/science/article/pii/S2666307424000305 |
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