English

应用科学学报 >

2025 , Vol. 43 >Issue 5: 828 - 848

DOI: https://doi.org/10.3969/j.issn.0255-8297.2025.05.010

信号与信息处理

面向医学图像的电子病历编码域隐藏加密设计

  • 郭昌豪 ,
  • 李梦 ,
  • 李豪杰 ,
  • 王欢欢 ,
  • 王新菲
展开
  • 1. 重庆工商大学 数学与统计学院, 重庆 400067;
    2. 重庆工商大学 统计智能计算与监测重庆市重点实验室, 重庆 400067

收稿日期: 2024-11-12

  网络出版日期: 2025-10-16

基金资助

重庆市自然科学基金面上项目（No. cstc2020jcyj-msxmX0162）； 重庆工商大学研究生科研创新项目（No. yjscxx2024-284-258, No. yjscxx2024-284-253, No. yjscxx2025-269-222）

收起

Design of Encoding-Domain Hidden Encryption for Electronic Medical Records in Medical Images

  • GUO Changhao ,
  • LI Meng ,
  • LI Haojie ,
  • WANG Huanhuan ,
  • WANG Xinfei
Expand
  • 1. School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China;
    2. Chongqing Key Laboratory of Statistical Intelligent Computing and Monitoring, Chongqing Technology and Business University, Chongqing 400067, China

Received date: 2024-11-12

  Online published: 2025-10-16

Fold

摘要

随着远程医疗的快速发展，患者隐私数据的安全管理与传输面临严峻挑战。为实现电子病历与医学图像的统一管理与保密存储，提出了一种针对多模态医疗数据的编码域隐藏加密方案。首先设计了一种基于UTF-8编码与位置数制分解的字符串图像化算法，将电子病历转换为编码图像，保障数据的隐私性和安全性。其次提出一种改进的HiNet可逆网络，将医学图像嵌入编码图像中，并通过引入Kullback-Leibler (KL)散度约束分布距离，提升图像嵌入与重建的精确性和鲁棒性。最后为进一步增强载密图像的安全性，设计了一种基于逻辑-正余弦混沌系统的Bit级加密算法，利用高敏感性和非线性特性实现强加密保护。实验结果表明，所提方案不仅能够有效保障数据的安全性，而且可同时实现电子病历的无损访问与医学图像的高质量恢复，为远程医疗中的多模态数据安全管理提供了更高的保密性。

关键词： Bit级混沌加密; UTF-8编码; 可逆网络隐写; 多模态数据; 隐私保护

本文引用格式

郭昌豪 , 李梦 , 李豪杰 , 王欢欢 , 王新菲 . 面向医学图像的电子病历编码域隐藏加密设计[J]. 应用科学学报, 2025 , 43(5) : 828 -848 . DOI: 10.3969/j.issn.0255-8297.2025.05.010

Abstract

With the rapid development of telemedicine, the secure management and transmission of patient privacy data face significant challenges. To achieve unified management and secure storage of electronic medical records (EMRs) and medical images, this paper proposes an encoding-domain hidden encryption scheme for multimodal medical data. Specifically, a string-to-image transformation algorithm based on UTF-8 encoding and positional numeral decomposition is designed to convert EMRs into encoded images, ensuring data privacy and security. To facilitate integrated management of multimodal data, an improved HiNet reversible network is introduced to embed medical images into encoded images. By incorporating the Kullback-Leibler (KL) divergence to constrain the distribution distance, the scheme enhances the accuracy and robustness of image embedding and reconstruction. Furthermore, to strengthen the security of the encoded images, a bit-level encryption algorithm based on the logic-sine-cosine chaotic system is designed, leveraging its high sensitivity and nonlinear characteristics for robust encryption. Experimental results demonstrate that the proposed encoding-domain hidden encryption scheme effectively ensures data security while enabling lossless access to EMRs and high-quality recovery of medical images, offering enhanced confidentiality for secure management of multimodal data in telemedicine.

Key words： Bit-level chaotic encryption; UTF-8 encoding; reversible network steganography; multimodal data; privacy protection

参考文献

[1] Alawida M, Samsudin A, Sen Teh J, et al. A new hybrid digital chaotic system with applications in image encryption [J]. Signal Processing, 2019, 160: 45-58.
[2] Bezerra J I M, Machado G, Molter A, et al. A novel simultaneous permutation-diffusion image encryption scheme based on a discrete space map [J]. Chaos Solitons & Fractals, 2023, 168: 113160.
[3] 苑紫烨, 邱宝林, 叶妤, 等. 面向编码伪装的鲁棒无载体图像隐写方法[J]. 应用科学学报, 2024, 42(3): 469-485. Yuan Z Y, Qiu B L, Ye Y, et al. A robust coverless image steganography method for coding camouflage [J]. Journal of Applied Sciences, 2024, 42(3): 469-485. (in Chinese)
[4] 张付霞. 基于多维桶分组技术改进算法对电子病历隐私信息研究[J]. 计算机应用与软件, 2024, 41(2): 86-92. Zhang F X. Privacy information of electronic medical record based on improved algorithm of multi-dimensional bucket grouping technology [J]. Computer Applications and Software, 2024, 41(2): 86-92. (in Chinese)
[5] Zha H Y, Zhang W M, Yu N H, et al. Enhancing image steganography via adversarial optimization of the stego distribution [J]. Signal Processing, 2023, 212: 109155.
[6] 周娜, 成茗, 贾孟霖, 等. 基于缩略图加密和分布式存储的医学图像隐私保护[J]. 计算机应用, 2023, 43(10): 3149-3155. Zhou N, Cheng M, Jia M L, et al. Medical image privacy protection based on thumbnail encryption and distributed storage [J]. Journal of Computer Applications, 2023, 43(10): 3149- 3155. (in Chinese)
[7] Zhu J R, Kaplan R, Johnson J, et al. Hidden: hiding data with deep networks [DB/OL]. (2018-07-26) [2024-11-12]. Http://arxiv.org/abs/1807.09937.
[8] Zhang K A, Cuesta-Infante A, Xu L, et al. SteganoGAN: high capacity image steganography with GANs [DB/OL]. (2019-01-30) [2024-11-12]. Http://arxiv.org/abs/1901.03892.
[9] Singh B, Sharma P K, Huddedar S A, et al. StegGAN: hiding image within image using conditional generative adversarial networks [J]. Multimedia Tools and Applications, 2022, 81(28): 40511-40533.
[10] Jing J P, Deng X, Xu M, et al. HiNet: deep image hiding by invertible network [C]//IEEE/CVF International Conference on Computer Vision, 2021: 4713-4722.
[11] Dou Y Q, Li M. An image encryption algorithm based on a novel 1D chaotic map and compressive sensing [J]. Multimedia Tools and Applications, 2021, 80(16): 24437-24454.
[12] Hosny K M, Kamal S T, Darwish M M, et al. A color image encryption technique using block scrambling and chaos [J]. Multimedia Tools and Applications, 2022, 81(1): 505-525.
[13] Alawida M. A novel chaos-based permutation for image encryption [J]. Journal of King Saud University-Computer and Information Sciences, 2023, 35(6): 101595.
[14] Muthu J S, Murali P. A new chaotic map with large chaotic band for a secured image cryptosystem [J]. Optik, 2021, 242: 167300.
[15] Wang X Y, Guan N N, Yang J J, et al. Image encryption algorithm with random scrambling based on one-dimensional logistic self-embedding chaotic map [J]. Chaos Solitons & Fractals, 2021, 150(3): 111117.
[16] Dinh L, Krueger D, Bengio Y, et al. NICE: non-linear independent components estimation [DB/OL]. (2014-10-30) [2024-11-12]. Http://arxiv.org/abs/1410.8516.
[17] Van Der Ouderaa T F A, Worrall D E. Reversible GANs for memory-efficient imageto-image translation [C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 4720-4728.
[18] Guan Z Y, Jing J P, Deng X, et al. DeepMIH: deep invertible network for multiple image hiding [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(1): 372- 390.
[19] Li F Y, Yang S, Zhang X P, et al. iSCMIS: spatial-channel attention based deep invertible network for multi-image steganography [J]. IEEE Transactions on Multimedia, 2024, 26(7): 3137-3152.
[20] 张倩楠, 王蒙. 基于条件可逆神经网络的多模态医学图像融合[J]. 计算机测量与控制, 2025, 33(1): 147-162. Zhang Q N, Wang M, et al. Multi-modal medical image fusion based on conditional reversible neural networks [J]. Computer Measurement & Control, 2025, 33(1): 147-162. (in Chinese)
[21] Weng X Y, Li Y Z, Chi L, et al. High-capacity convolutional video steganography with temporal residual modeling [C]//ACM International Conference on Multimedia Retrieval (ICMR), 2019: 87-95.
[22] Wang X T, Yu K, Wu S X, et al. ESRGAN: enhanced super-resolution generative adversarial networks [DB/OL]. (2018-09-17) [2024-11-12]. Http://arxiv.org/abs/1809.00219.
[23] Hu R W, Xiang S J. Reversible data hiding by using CNN prediction and adaptive embedding [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12): 10196-10208.
[24] Tang Y B, Tang Y X, Xiao J, et al. XLSor: a robust and accurate lung segmentor on chest Xrays using criss-cross attention and customized radiorealistic abnormalities generation [DB/OL]. (2019-04-19) [2024-11-12]. Http://arxiv.org/abs/1904.09229v1.
[25] Solbach M D, Tsotsos J K. Vision-based fallen person detection for the elderly [C]//IEEE International Conference on Computer Vision Workshops, 2017: 433-1442.
[26] Hossain K F, Kamran S A, Ong J, et al. Revolutionizing space health (Swin-FSR): advancing super-resolution of fundus images for SANS visual assessment technology [DB/OL]. (2023-08-11) [2024-11-12]. Http://arxiv.org/abs/2308.06332.
[27] Baluja S. Hiding images in plain sight: deep steganography [C]//31st Annual Conference on Neural Information Processing Systems (NIPS), 2017: 2069-2079.
[28] Rehman A U, Rahim R, Nadeem M S, et al. End-to-end trained CNN encode-decoder networks for image steganography [C]//European Conference on Computer Vision (ECCV) Workshops, 2018: 723-729.
[29] 王越, 安新磊, 施倩倩, 等. 基于一个复混沌系统的图像加密算法[J]. 复杂系统与复杂性科学, 2024, 21(3): 77-84. Wang Y, An X L, Shi Q Q, et al. Image encryption algorithm based on a complex chaotic system [J]. Complex Systems and Complexity Science, 2024, 21(3): 77-84. (in Chinese)
[30] 王越, 安新磊, 施倩倩, 等. 基于一个四维超混沌系统的图像加密算法[J]. 计算机仿真, 2024, 41(4): 237-244, 259. Wang Y, An X L, Shi Q Q, et al. Image encryption algorithm based on a four-dimensional hyper chaotic system [J]. Computer Simulation, 2024, 41(4): 237-244, 259. (in Chinese)
[31] 施倩倩, 张莉. 基于共存吸引子的图像加密算法[J]. 河北师范大学学报(自然科学版), 2022, 46(3): 239-250. Shi Q Q, Zhang L. Image encryption algorithm based on coexisting attractor [J]. Journal of Hebei Normal University (Natural Science), 2022, 46(3): 239-250. (in Chinese)
[32] Li S, Alvarez G. Some basic cryptographic requirements for chaos-based cryptosystems [J]. International Journal of Bifurcation and Chaos, 2006, 16(8): 2129-2151.
Options
文章导航

/

〈 〉