Semi-tensor product compression sensing integrated to verifiable image encryption method

Wenying Wen; Yukun Hong; Yuming Fang; Yushu Zhang; Zheng Wan

doi:10.11834/jig.210442

Authentication Protection | Views : 0 下载量: 133 CSCD: 6

PDF
Export
Share
Collection
Album

Semi-tensor product compression sensing integrated to verifiable image encryption method
Vol. 27, Issue 1, Pages: 215-225(2022)
Received：22 June 2021，

Revised：2021-9-6，

Accepted：13 September 2021，

Published：16 January 2022
DOI： 10.11834/jig.210442
稿件说明：

移动端阅览

DOI：

Wenying Wen, Yukun Hong, Yuming Fang, Yushu Zhang, Zheng Wan. Semi-tensor product compression sensing integrated to verifiable image encryption method[J]. Journal of Image and Graphics, 2022, 27(1): 215-225. DOI： 10.11834/jig.210442.

摘要

目的

物联网(internet of things

IoT)感知层获取数据时存在资源受限的约束，同时数据常常遭受泄露和非法篡改。数据一旦遭到破坏，将对接收者造成很大的影响，甚至可能会比没有收到数据更加严重。针对IoT数据获取面临的能耗和安全问题，提出一种基于半张量积压缩感知的可验证图像加密方法。

方法

首先采用级联混沌系统生成测量矩阵和验证矩阵，测量矩阵以半张量积压缩感知的方式进行采样得到观测值矩阵。利用Arnold置乱观测值矩阵得到最终密文信号，与此同时由验证矩阵生成消息验证码一同在公共信道传输，将由级联混沌系统生成的测量矩阵、验证矩阵以及Arnold置乱的参数的初始种子作为密钥在安全信道上传输。

结果

密钥空间分析、密钥敏感性分析、图像熵分析、直方图分析、相关性分析、身份验证分析、压缩率分析的实验结果显示：相比于两种对比方法，本文算法加密后图像的熵值更接近于8，而对应密文图像像素之间的相关系数更接近于0。

结论

本文的可验证加密算法结合了半张量压缩感知的优点，在有效减少数据采样能耗的同时保证了数据在传输过程中的安全性与完整性。

Abstract

Objective

Big multimedia data is acquired via various multimedia sensors and mobile devices nowadays. It is necessary to implement low-cost sampling compression coding due to the limited computing resources and large data volume of sensors and mobile devices.. Illegal applications from extracting valuable information is to be prevented during sampling and transmission. Compressed sensing has credited for data collection in the internet of things(IoT). As a novel signal acquisition theory

compressed sensing

has been focused on. The compressed sensing framework is a sort of encryption scheme. Compared with conventional encryption schemes

compressed sensing encryption schemes have their advantages

such as low computational cost of encryption process

synchronized realization of encryption and compression

and robustness of ciphertext. The compressed sensing framework for information authority will be concentrated. A way for the recipient has been confirmed the integrity of the information for information tampers. The emerging verification code is to check whether the content of the message has been changed in the process of message delivery

regardless of the accidental or deliberate attack change. The identity verification of the message source is to confirm the source of the message. A sequence value of a certain length is first obtained via the initial compressed message. The sequence value of the same length is generated for the verified message again in accordance with the one mapping method. The initial sequence to get the results have been compared with incomplete data. But

conventional methods are ineffective due to the avalanche effect of compressed sensing. In the compressed sensing framework

the measured value is transmitted instead of the original signal. The receiving end receives the measured value

and the original signal needs to be restored with a restoration algorithm. Compressed sensing can only make the restored signal approximate to the original signal. The message verification sequence generated by the receiving end is completely different from the received verification sequence. In the IoT perception layer

there are some constraints in data acquisition resource and suffer from privacy leakage and illegal tampering. To resolve energy consumption and security in IoT data acquisition

a verifiable image encryption method has been illustrated based on semi-tensor product compression sensing.

Method

First

The measurement matrix and the verification matrix based on the cascade chaotic system are used to sample the sparse signal in terms of semi-tensor product application. The measured value matrix is further used for Arnold scrambling to obtain the final secret image. Simultaneously

the identity verification code is generated by the identity verification matrix and transmitted on the public channel

and the initial seed of the cascade chaotic system is as the key for transmission on the secure channel.

Result

Key space analysis

key sensitivity analysis

image entropy analysis

histogram analysis

correlation analysis

identity verification analysis

compression rate analysis have been tested and analyzed overall. The demonstrated results show that the encrypted image entropy in the scheme illustrated is closer to 8

while the encrypted image correlation coefficient is close to 0.

Conclusion

The verifiable encryption algorithm has integrated the advantages of semi-tensor compression perception. The security and integrity of data transmission has been realized effectively in terms of the decrease of energy consumption of data sampling.

关键词

Keywords

references

Candes E J, Romberg J and Tao T. 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2): 489-509 [DOI: 10.1109/TIT.2005.862083]

Chen F, Wong K W, Liao X F and Xiang T. 2012. Period distribution of generalized discrete Arnold cat map for N = p e . IEEE Transactions on Information Theory, 58(1): 445-452 [DOI: 10.1109/TIT.2011.2171534].

Cheng D Z, Qi H S and Xue A C. 2007. A survey on semi-tensor product of matrices. Journal of Systems Science and Complexity, 20(2): 304-322 [DOI: 10.1007/s11424-007-9027-0]

Guesmi R, Farah M A B, Kachouri A and Samet M. 2016. A novel chaos-based image encryption using DNA sequence operation and Secure Hash Algorithm SHA-2. Nonlinear Dynamics, 83(3): 1123-1136 [DOI: 10.1007/s11071-015-2392-7]

Khalili M and Asatryan D. 2013. Colour spaces effects on improved discrete wavelet transform-based digital image watermarking using Arnold transform map. IET Signal Processing, 7(3): 177-187 [DOI: 10.1049/iet-spr.2012.0380]

Li C Q, Feng B B, Li S J, Kurths J and Chen G R. 2019a. Dynamic analysis of digital chaotic maps via state-mapping networks. IEEE Transactions on Circuits and Systems Ⅰ: Regular Papers, 66(6): 2322-2335 [DOI: 10.1109/TCSI.2018.2888688]

Li C Q, Tan K, Feng B B and Lu J H. 2021. The graph structure of the generalized discrete Arnold's cat map[J/OL]. IEEE Transactions on Computers. https://ieeexplore.ieee.org/document/9321487 https://ieeexplore.ieee.org/document/9321487 [ DOI: 10.1109/TC.2021.3051387 http://dx.doi.org/10.1109/TC.2021.3051387 ].

Li C Q, Zhang Y and Xie E Y. 2019b. When an attacker meets a cipher-image in 2018: a year in review. Journal of Information Security and Applications, 48: #102361 [DOI: 10.1016/j.jisa.2019.102361]

Luo Y L, Lin J, Liu J X, Wei D Q, Cao L, Zhou R L, Cao Y and Ding X M. 2019. A robust image encryption algorithm based on Chua's circuit and compressive sensing. Signal Processing, 161: 227-247 [DOI: 10.1016/j.sigpro.2019.03.022]

Mangia M, Pareschi F, Rovatti R and Setti G. 2018. Low-cost security of IoT sensor nodes with Rakeness-based compressed sensing: statistical and known-plaintext attacks. IEEE Transactions on Information Forensics and Security, 13(2): 327-340 [DOI: 10.1109/TIFS.2017.2749982]

Millerioux G, Amigo J M and Daafouz J. 2008. A connection between chaotic and conventional cryptography. IEEE Transactions on Circuits and Systems Ⅰ: Regular Papers, 55(6): 1695-1703 [DOI: 10.1109/TCSI.2008.916555]

Mohimani H, Babaie-Zadeh M and Jutten C. 2009. A fast approach for overcomplete sparse decomposition based on smoothed l 0 norm. IEEE Transactions on Signal Processing, 57(1): 289-301 [DOI: 10.1109/TSP.2008.2007606].

Rachlin Y and Baron D. 2008. The secrecy of compressed sensing measurements//Proceedings of 2008 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 ]

Shannon C E. 1949. Communication in the Presence of Noise. in Proceedings of the IRE. 37(1): 10-21 [DOI: 10.1109/JRPROC.1949.232969].

Wang G Y, Yu H and Yang D C. 2002. Decision table reduction based on conditional information entropy. Chinese Journal of Computers, 25(7): 759-766

王国胤, 于洪, 杨大春. 2002. 基于条件信息熵的决策表约简. 计算机学报, 25(7): 759-766 [DOI: 10.3321/j.issn:0254-4164.2002.07.013]

Wang L C, Li L X, Li J, Li J, Gupta B B and Liu X. 2019. Compressive sensing of medical images with confidentially homomorphic aggregations. IEEE Internet of Things Journal, 6(2): 1402-1409 [DOI: 10.1109/JIOT.2018.2844727]

Wei L F, Zhang K, Zhang L and Huang D M. 2018. A secure data forwarding protocol for data statistic services in multi-hop marine sensor networks. Fundamenta Informaticae, 157(1/2): 63-78 [DOI: 10.3233/FI-2018-1618]

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): 1-14 [DOI: 10.1016/j.sigpro.2020.107580]

Wen W Y, Zhang Y S, Fang Z J and Chen J X. 2015. Infrared target-based selective encryption by chaotic maps. Optics Communications, 341: 131-139 [DOI: 10.1016/j.optcom.2014.12.026]

Wu T and Ruland C. 2018. An improved authenticated compressive sensing imaging//Proceedings of 2018 IEEE 12th International Conference on Semantic Computing. Laguna Hills, USA: IEEE: 164-171 [ DOI: 10.1109/ICSC.2018.00031 http://dx.doi.org/10.1109/ICSC.2018.00031 ]

Xie D, Peng H P, 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]

Zhang Y S, He Q, Xiang Y, Zhang L Y, Liu B, Chen J X and Xie Y Y. 2018. Low-cost and confidentiality-preserving data acquisition for internet of multimedia things. IEEE Internet of Things Journal, 5(5): 3442-3451 [DOI: 10.1109/JIOT.2017.2781737]

Zhou Y C, Hua Z Y, Pun C M and Chen C L P. 2015. Cascade chaotic system with applications. IEEE Transactions on Cybernetics, 45(9): 2001-2012 [DOI: 10.1109/TCYB.2014.2363168]

Alert me when the article has been cited

提交

Image encryption algorithm based on the semi-tensor product

Smooth l

Reversible data hiding for encrypted images with Kd-tree and entropy coding

Reversible data hiding in encrypted images using variable prediction

Chaotic image encryption algorithm based on elliptic curve and adaptive DNA coding