浏览全部资源
扫码关注微信
Received：06 May 2022，
Revised：2022-7-22，
Accepted：29 July 2022，
Published：16 March 2023
移动端阅览
目的
2
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
2
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
2
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8~10 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
2
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
2
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
2
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 receiving end
and partial data is used for STP-CS reconstruction to recover sensitive data for pre-reconstructed medical images. The data remaining is employed to extract confidential patient information and non-sensitive data
which allows that the pre-reconstructed medical images to be recovered. Considering that the attacker may tamper with the image data in the image data transmission
the data integrity authentication step is added to the image data
which can accurately complete the authentication under the interference of quantization noise and transmission noise. However
the tampered image data is still challenged to be authenticated.
Result
2
This scheme can achieve the recovery of STP-CS-reconstructed images to high-quality images with low-complexity reconstruction
and verify the integrity of carrier images and embedded secret information with high-efficiency authentication. STP-CS not only optimizes the storage space of the measurement matrix by multiple
but also improves the real-time performance of reconstruction. To verify the effectiveness of our scheme proposed
we select 4 MRI (megnetic resonace imaging images) targeted on different parts of the human body
and our scheme is then evaluated with 3 kind of popular image recovery algorithms on these images. The experimental results show that the peak signal-to-noise ratio (PSNR) of this algorithm is optimized further
and the performance of this algorithm is improved and valued by about 8~10 dB compared to the second method. To analyze the security of the proposed algorithm
we introduce 3 sort of measurement methods like 1) key space
2) histogram
and 3) entropy. Experimental results demonstrate that the key space of this scheme is 10
16
×10
16
×10
24
×10
24
= 10
80
≈ 2
240
(≫ 2
100
)
which is sufficient to prevent brute force attacks. The histograms of the encrypted image and the image are flat and cosistent with non-embedded secret information
which verifies the proposed scheme is enough to prevent attackers from obtaining histogram-based valuable information. Additionally
the entropy of all encrypted images is close to the benched value of 8
and thus the proposed scheme has the ability to resist entropy cryptanalysis. To analyze its robustness
1) cropping acctack
2) JPEG (joint photographic experts group compression)
and 3) noise interference are selected
and the experimental results show that the interference against both of cropping and noise attacks have good robustness there
and the bit error rate (BER) of secret information is less than 5%
which ensures the integrity of secret information. To validate the effectiveness of the algorithm
the experiments demonstrate the reconstruction quality of medical images at different scale of resolutions
and the experimental results show that higher resolution is beneficial to image reconstruction quality. The size of the half-tensor measurement matrix can be matched to the optimization of image accuracy
the change of resolution does not enforce the degradation of the reconstruction quality of the images
as well as the embedding of secret information is guranteed.
Conclusion
2
Our scheme can optimize data confidentiality and provide new features in relevant to step-by-step recovery
pixel compression
and high-capacity secret information embedding.
Ahmad T, Holil M, Wibisono W and MuslimI R. 2013. An improved Quad and RDE-based medical data hiding method//Proceedings of 2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM). Yogyakarta, Indonesia: IEEE: 141-145 [ DOI: 10.1109/CyberneticsCom.2013.6865798 http://dx.doi.org/10.1109/CyberneticsCom.2013.6865798 ]
Anand A and Singh A K. 2022. SDH: secure data hiding in fused medical image for smart healthcare. IEEE Transactions on Computational Social Systems, 9(4): 1265-1273 [DOI: 10.1109/TCSS.2021.3125025]
Chai X L, Wu H Y, Gan Z H, Zhang Y S, Chen Y R and Nixon K W. 2020. An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding. Optics and Lasers in Engineering, 124: #105837 [DOI: 10.1016/j.optlaseng.2019.105837]
Donoho D L. 2006. Compressed sensing. IEEE Transactions on Information Theory, 52(4): 1289-1306 [DOI: 10.1109/TIT.2006.871582]
Gan Z H, Chai X L, Zhang J T, Zhang Y S and Chen Y R. 2020. An effective image compression-encryption scheme based on compressive sensing (CS) and game of life (GOL). Neural Computing and Applications, 32(17): 14113-14141 [DOI: 10.1007/s00521-020-04808-8]
Khari M, Garg A K, Gandomi A H, Gupta R, Patan R and Balusamy B. 2020. Securing data in internet of things (IoT) using cryptography and steganography techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(1): 73-80 [DOI: 10.1109/TSMC.2019.2903785]
Li M, Yu S C, Zheng Y, Ren K and Lou W J. 2013. Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE Transactions on Parallel and Distributed Systems, 24(1): 131-143 [DOI: 10.1109/TPDS.2012.97]
Liu J H, Ma J H, Xiang Y, Zhou W L and Huang X Y. 2021. Authenticated medical documents releasing with privacy protection and release control. IEEE Transactions on Dependable and Secure Computing, 18(1): 448-459 [DOI: 10.1109/TDSC.2019.2892446]
Oleshchuk V. 2019. Secure and privacy preserving pattern matching in distributed cloud-based data storage//Proceedings of the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). Metz, France: IEEE: 820-823 [ DOI: 10.1109/IDAACS.2019.8924356 http://dx.doi.org/10.1109/IDAACS.2019.8924356 ]
Ou B, Yin Z X and Xiang S J. 2022. Overview of reversible data hiding in plaintext image. Journal of Image and Graphics, 27(1): 111-124
欧博, 殷赵霞, 项世军. 2022. 明文图像可逆信息隐藏综述. 中国图象图形学报, 27(1): 111-124 [DOI: 10.11834/jig.210384]
Rachlin Y and Baron D. 2008. The secrecy of compressed sensing measurements//Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing. Monticello, USA: IEEE: 813-817 [ DOI: 10.1109/ALLERTON.2008.4797641 http://dx.doi.org/10.1109/ALLERTON.2008.4797641 ]
Wen W Y, Hong Y K, Fang Y M, Li M and Li M. 2020. A visually secure image encryption scheme based on semi-tensor product compressed sensing. Signal Processing, 173: #107580 [DOI: 10.1016/j.sigpro.2020.107580]
Wen W Y, Hong Y K, Fang Y M, Zhang Y S and Wan Z. 2022. Semi-tensor product compression sensing integrated to verifiable image encryption method. Journal of Image and Graphics, 27(1): 215-225
温文媖, 洪宇坤, 方玉明, 张玉书, 万征. 2022. 结合半张量积压缩感知的可验证图像加密. 中国图象图形学报, 27(1): 215-225 [DOI: 10.11834/jig.210442]
Wu T and Ruland C. 2017. Authenticated compressive sensing imaging//2017 International Symposium on Networks, Computers and Communications (ISNCC). Marrakech, Morocco: IEEE: 1-6 [ DOI: 10.1109/ISNCC.2017.8072009 http://dx.doi.org/10.1109/ISNCC.2017.8072009 ]
Xie D, Peng H, Li L X and Yang Y X. 2016. Semi-tensor compressed sensing. Digital Signal Processing, 58: 85-92[DOI: 10.1016/j.dsp.2016.07.003]
Yang Y, Xiao X X, Cai X and Zhang W M. 2020. A secure and privacy-preserving technique based on contrast-enhancement reversible data hiding and plaintext encryption for medical images. IEEE Signal Processing Letters, 27: 256-260 [DOI: 10.1109/LSP.2020.2965826]
Yin Z X, Xiang Y Z and Zhang X P. 2020. Reversible data hiding in encrypted images based on multi-MSB prediction and huffman coding. IEEE Transactions on Multimedia, 22(4): 874-884 [DOI: 10.1109/TMM.2019.2936314]
Zhan R X, Chau K Y, Lu Z M, Liu B B and Ip W H. 2008. Robust image hashing for image authentication based on DCT-DWT composite domain//Proceedings of 2008 Eighth International Conference on Intelligent Systems Design and Applications. Kaohsuing, China: IEEE: 119-122 [ DOI: 10.1109/ISDA.2008.66 http://dx.doi.org/10.1109/ISDA.2008.66 ]
Zhang Y, Luo X Y, Guo Y Q, Qin C and Liu F L. 2020. Multiple robustness enhancements for image adaptive steganography in lossy channels. IEEE Transactions on Circuits and Systems for Video Technology, 30(8): 2750-2764 [DOI: 10.1109/TCSVT.2019.2923980]
Zhang Y, Qiu M K, Tsai C W, Hassan M M and Alamri A. 2017. Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1): 88-95 [DOI: 10.1109/JSYST.2015.2460747]
Zhang Y S, Wang P, Huang H, Zhu Y W, Xiao D and Xiang Y. 2021. Privacy-assured FogCS: chaotic compressive sensing for secure industrial big image data processing in fog computing. IEEE Transactions on Industrial Informatics, 17(5): 3401-3411 [DOI: 10.1109/TII.2020.3008914]
Zhu L Y, Luo X Y, Zhang Y, Yang C F and Liu F L. 2022. Inverse interpolation and its application in robust image steganography. IEEE Transactions on Circuits and Systems for Video Technology, 32(6): 4052-4064 [DOI: 10.1109/TCSVT.2021.3107342]
相关文章
相关作者
相关机构
京公网安备11010802024621
