Hiding cipher-images generated by 2-D compressive sensing with a multi-embedding strategy

Abstract In terms of 2-D compressive sensing (CS), multi-embedding strategy and chaotic systems, a novel color image encryption scheme to generate visually meaningful cipher image is proposed in this paper. Firstly, red, green and blue components of color image are compressed to obtain measurement value matrices by virtue of 2-D CS, respectively. Next, the assembled measurement value matrix is shuffled in bit and pixel level and diffused to get a compressed cipher image. Subsequently, two embedding methods are presented to get visually meaningful color or grayscale cipher image by embedding cipher image into color or grayscale carrier image. Compared with volume of plain image, that of carrier image and final cipher image has reduced more than 50%. Moreover, a 4-D hyperchaotic system is applied to generate measurement matrices and control pixel-level confusion, Logistic system is utilized to control bit-level confusion and produce key matrix combined with Local Binary Pattern (LBP), and their initial values are computed by SHA 256 hash value of plain image, which enhances the relationship between plain image and encryption process. Embedding hash value into carrier image prevents extra transmission and storage. Simulation results and security analyses demonstrate the effectiveness and security of the proposed algorithm.

[1]  Guanrong Chen,et al.  Dynamic Analysis of Digital Chaotic Maps via State-Mapping Networks , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Li-Hua Gong,et al.  Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing , 2014 .

[3]  Li Guo,et al.  Joint image compression–encryption scheme using entropy coding and compressive sensing , 2018, Nonlinear Dynamics.

[4]  Yiran Chen,et al.  An image encryption algorithm based on chaotic system and compressive sensing , 2018, Signal Process..

[5]  Xiaofeng Liao,et al.  A fast and efficient approach to color-image encryption based on compressive sensing and fractional Fourier transform , 2017, Multimedia Tools and Applications.

[6]  Shiguo Lian,et al.  A novel color image encryption algorithm based on DNA sequence operation and hyper-chaotic system , 2012, J. Syst. Softw..

[7]  Di Wang,et al.  Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform , 2015 .

[8]  Jiashu Zhang,et al.  Iterative gradient projection algorithm for two-dimensional compressive sensing sparse image reconstruction , 2014, Signal Process..

[9]  Hui Wang,et al.  A visually secure image encryption scheme based on parallel compressive sensing , 2019, Signal Process..

[10]  Ali Kanso,et al.  An algorithm for encryption of secret images into meaningful images , 2017 .

[11]  Di Xiao,et al.  Secure binary arithmetic coding based on digitalized modified logistic map and linear feedback shift register , 2015, Commun. Nonlinear Sci. Numer. Simul..

[12]  Sos S. Agaian,et al.  Local Shannon entropy measure with statistical tests for image randomness , 2013, Inf. Sci..

[13]  Xingyuan Wang,et al.  A color image encryption with heterogeneous bit-permutation and correlated chaos , 2015 .

[14]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[15]  Tian Xianglin.  An Optimization Algorithm for Measurement Matrix in Compressed Sensing , 2015 .

[16]  Hamid Abrishami Moghaddam,et al.  Two-dimensional random projection , 2011, Signal Process..

[17]  Yong Xiang,et al.  A compression-diffusion-permutation strategy for securing image , 2018, Signal Process..

[18]  Chengqing Li,et al.  When an attacker meets a cipher-image in 2018: A Year in Review , 2019, J. Inf. Secur. Appl..

[19]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[20]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[21]  Kehui Sun,et al.  A novel bit-level image encryption algorithm based on 2D-LICM hyperchaotic map , 2018, Signal Process..

[22]  Yan Zhou,et al.  2D compressive sensing and multi-feature fusion for effective 3D shape retrieval , 2017, Inf. Sci..

[23]  Congxu Zhu,et al.  A novel image encryption scheme based on improved hyperchaotic sequences , 2012 .

[24]  Hao Jiang,et al.  Double-image compression and encryption algorithm based on co-sparse representation and random pixel exchanging , 2018, Optics and Lasers in Engineering.

[25]  X. Tong,et al.  A new algorithm of the combination of image compression and encryption technology based on cross chaotic map , 2013 .

[26]  Khumanthem Manglem Singh,et al.  Visually Meaningful Multi-image Encryption Scheme , 2018, Arabian Journal for Science and Engineering.

[27]  Chang'e Dong,et al.  Color image encryption using one-time keys and coupled chaotic systems , 2014, Signal Process. Image Commun..

[28]  Duqu Wei,et al.  A robust image encryption algorithm based on Chua's circuit and compressive sensing , 2019, Signal Process..

[29]  Xiubo Chen,et al.  Eliminating the texture features in visually meaningful cipher images , 2018, Inf. Sci..

[30]  Yiran Chen,et al.  A visually secure image encryption scheme based on compressive sensing , 2017, Signal Process..

[31]  Dao-jun Han,et al.  A chaotic image encryption algorithm based on 3-D bit-plane permutation , 2018, Neural Computing and Applications.

[32]  Yiran Chen,et al.  An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata , 2018, Neural Computing and Applications.

[33]  Lisheng Xu,et al.  Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression , 2018 .

[34]  Hongjun Liu,et al.  Asymmetric color image encryption scheme using 2D discrete-time map , 2015, Signal Process..

[35]  Seyed Mohammad Seyedzadeh,et al.  A novel color image encryption algorithm based on spatial permutation and quantum chaotic map , 2015, Nonlinear Dynamics.

[36]  Zhibin Pan,et al.  Central pixel selection strategy based on local gray-value distribution by using gradient information to enhance LBP for texture classification , 2019, Expert Syst. Appl..

[37]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[38]  Chen Chen,et al.  An improved image encryption algorithm with finite computing precision , 2020, Signal Process..

[39]  Jian Liu,et al.  Characteristic analysis of the fractional-order hyperchaotic complex system and its image encryption application , 2020, Signal Process..

[40]  Hai Jiang,et al.  Permutation Meets Parallel Compressed Sensing: How to Relax Restricted Isometry Property for 2D Sparse Signals , 2013, IEEE Transactions on Signal Processing.

[41]  V. Masilamani,et al.  An efficient visually meaningful image encryption using Arnold transform , 2016, 2016 IEEE Students’ Technology Symposium (TechSym).

[42]  Yiran Chen,et al.  A color image cryptosystem based on dynamic DNA encryption and chaos , 2019, Signal Process..

[43]  Hejiao Huang,et al.  Image Encryption Using Josephus Problem and Filtering Diffusion , 2019, IEEE Access.

[44]  Kehui Sun,et al.  A fast image encryption algorithm based on compressive sensing and hyperchaotic map , 2019, Optics and Lasers in Engineering.

[45]  Jiantao Zhou,et al.  A Review of Compressive Sensing in Information Security Field , 2016, IEEE Access.

[46]  Feng Hao,et al.  Cryptanalysis of a Chaotic Image Encryption Algorithm Based on Information Entropy , 2018, IEEE Access.

[47]  Yushu Zhang,et al.  An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding , 2020 .