A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

Mohamed Abdel Hameed , M. Hassaballah , Riem Abdelazim , Aditya Kumar Sahu
{"title":"A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement","authors":"Mohamed Abdel Hameed ,&nbsp;M. Hassaballah ,&nbsp;Riem Abdelazim ,&nbsp;Aditya Kumar Sahu","doi":"10.1016/j.ijcce.2024.08.002","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"5 ","pages":"Pages 379-397"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666307424000305/pdfft?md5=c77d0ff8e6f6ef8f125596b01d1f19d8&pid=1-s2.0-S2666307424000305-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Cognitive Computing in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666307424000305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

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.

基于逆向神经密码学和使用最小有效位替换的数字签名的新型医学隐写技术
随着近年来技术的进步,保护敏感的医疗数据已成为一项挑战。特别是,医疗信息安全方面最严重的问题之一是保护医疗内容,如病人的隐私。随着医疗信息的普及,必须制定安全措施来保护信息的保密性、完整性和可用性。最近有人提出了图像隐写术,作为医疗记录的额外数据保护机制。本文介绍了一种针对 DICOM 医学图片的数据隐藏方法。为确保保密性,我们使用带有 SHA-256 的对抗神经加密算法(ANC-SHA-256)将 RGB 病人图片加密并隐藏在医疗图片的非兴趣区域(RONI)内。为确保匿名性,我们在嵌入前使用 ANC-SHA-256 对 RGB 患者图片进行加密。我们采用 256 位(SHA-256)安全散列方法,从与 DICOM 文件链接的信息中生成数字签名,以验证医疗图片的真实性和完整性。我们使用不同的医疗数据集(包括 MRI、CT、X 光和超声波封面图片)进行了许多测试,以评估视觉质量。LFW 数据集被选为患者隐藏图片。所提出的方法在视觉质量测量方面表现良好,包括 PSNR 平均值为 67.55,NCC 平均值为 0.9959,SSIM 平均值为 0.9887,UQI 平均值为 0.9859,APE 平均值为 3.83。在六个医疗评估类别中,该方法在这些视觉质量指标(PSNR、MSE 和 SSIM)方面优于当前最先进的技术。此外,所提出的方法在提供出色视觉质量的同时,还能抵御物理调整、直方图分析和其他几何威胁,如裁剪、旋转和缩放。最后,它在远程医疗应用中特别有效,在通过互联网远程传输电子病历(EPR)过程中实现了高达 99% 的安全性,从而保护了病人的隐私和数据完整性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
13.80
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信