面向医学图像加密域大容量信息隐藏与认证方法  

Large-capacity data hiding and authentication method in encryted domain for medical image

作　　者：温文媖[1] 邹孟雷 方玉明[1] 张玉书 左一帆 Wen Wenying;Zou Menglei;Fang Yuming;Zhang Yushu;Zuo Yifan(School of Information Management,Jiangxi University of Finance and Economics,Nanchang 330013,China;College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]江西财经大学信息管理学院,南昌330013 [2]南京航空航天大学计算机科学与技术学院,南京211106

出　　处：《中国图象图形学报》2023年第3期716-733,共18页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61961022,61961021,61906079);江西省主要学科学术和技术带头人培养计划项目(20204BCJL23036);江西省博士后科研项目(2020KY14);江西省教育厅基金项目(GJJ210502);江西省研究生教改项目(JXYJG-2021-101)。

摘　　要：目的远程医疗诊断是通过将患者的医学图像、病历以及诊断结果等敏感信息分享给医生或医疗机构,达到远程辅助治疗的目的。然而,在医疗数据分享过程中,患者的敏感信息易受篡改或伪造,导致信息的保密性、完整性和隐私性受到威胁,严重影响医生对患者的诊断。针对以上问题,利用医学图像高冗余特性,本文提出一种加密域大容量信息隐藏与高效认证方案。方法该方案结合半张量积压缩感知(semi-tensor product compressed sensing,STP-CS)与大容量秘密数据嵌入方式,将载体图像加密域腾出大容量空间用以嵌入患者信息。将医学图像数据分为非敏感数据和敏感数据两部分,通过传输非敏感数据以及STP-CS重构获取敏感数据实现云端与医院之间数据共享。结果本方案可实现以低复杂度的重构方式将STP-CS重建图像恢复到高质量图像,并以高效率的认证方式验证载体图像及嵌入秘密信息的完整性。为了验证算法的有效性,与优秀的图像恢复算法在不同部位MRI(megnetic resonace imaging)图像进行测试评估。实验结果表明,本文算法得到的载密图像能腾出约3.75 bit/像素的空间嵌入秘密信息,峰值信噪比均优于对比方法,相比于性能第2的方法提高了约810 dB。此外,本文算法恢复医学图像同样优于对比方法,有潜力应用于实际的远程医疗诊断。结论本文方法不仅提高数据的机密性,而且可获得现有医学图像共享方案中不具备的新功能,包括逐步恢复、像素压缩和大容量秘密信息嵌入等。Objective Telemedicine diagnosis has become one of heath seeking behaviours in big data era. To make a quick responsed accurate diagnosis, telemedicine diagnosis can used for the sharing of sensitive information to doctors or medical institutions in terms of a large amount of sensitive information of patients like patient identity information, medical profiles and medical-relevant images. The medical sensitive information security is required for transmiting. However, patients’ sensitive information is vulnerable to be tampered or forged in the process of medical data sharing, which affects doctors’ diagnosis of patients due to the threatens of the confidentiality, integrity and privacy of information. Therefore, telemedicine diagnosis is challenged to information preservation and the integrity of medical data. Method To resolve the problems mentioned above, we develop a cryptographic domain high-capacity information hiding scheme. To leak out about 3.75 bit/pixel of space in the encrypted domain of carrier images for embedding patient sensitive information, the scheme is coordinated semi-tensor product compressed sensing (STP-CS)-integrated to high-capacity secret data embedding method. A low-order Gaussian chaotic matrix is constructed to compress and sample medical images with a semi-tensor product, whereas the storage space of the measurement matrix can be reduced exponentially and the real-time reconstruction at the receiving end can be improved as well. Residual value prediction is uitilized to provide data embedding space, and the processes of data extraction and image restoration are splitted. Specifically, the medical image data is segmented into two categories: 1) sensitive data and 2) non-sensitive in terms of the prediction residual matrix. At the receiving end, medical image information is initially recovered via sensitive data-reconstructed, and the residual matrix-based medical images are then recovered to reconstruction capability-optimized image sharing scheme. The data is split at the receiv

关 键 词：医学图像 大容量信息隐藏 半张量积压缩感知(STP-CS) 安全认证 逐步重建 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]