An Image Compression-Encryption Algorithm Based on Cellular Neural Network and Compressive Sensing

In this paper, an image encryption algorithm on the basis of cellular neural networks (CNN) and compressive sensing (CS) is proposed. Firstly, four CNN with hyper chaotic behavior is introduced to generate chaotic sequence. Then, the index of the sorted chaotic sequence is used to control the generation of measurement matrix in CS procedure. Moreover, Lissajous map is served to produce asymptotic deterministic random measurement matrix instead of the common random measurement matrix. In addition, the chaotic sequence is normalized to 8-bit integer to diffuse the result after applying CS operation on the plain image, and the image after compression and encryption is obtained. The simulation results and analysis verify the proposed algorithm owns good security and ideal performance.

[1]  Minghui Du,et al.  A symmetrical image encryption scheme in wavelet and time domain , 2015, Commun. Nonlinear Sci. Numer. Simul..

[2]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[3]  Yi Cao,et al.  An efficient and self-adapting colour-image encryption algorithm based on chaos and interactions among multiple layers , 2018, Multimedia Tools and Applications.

[4]  Jianhua Wu,et al.  Novel hybrid image compression–encryption algorithm based on compressive sensing , 2014 .

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

[6]  Mohammad Hossein Moattar,et al.  Color image encryption based on hybrid hyper-chaotic system and cellular automata , 2017 .

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

[8]  F. Long,et al.  Grayscale image encryption based on Latin square and cellular neural network , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[9]  Jim Harkin,et al.  Counteracting Dynamical Degradation of Digital Chaotic Chebyshev Map via Perturbation , 2017, Int. J. Bifurc. Chaos.

[10]  Wenjiang Pei,et al.  The asymptotic deterministic randomness , 2007 .

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

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

[13]  Fei Long,et al.  CNN-based color image encryption algorithm using DNA sequence operations , 2017, 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC).

[14]  Jim Harkin,et al.  Cryptanalysis and Improvement of a Chaotic Map-Control-Based and the Plain Image-Related Cryptosystem , 2019 .

[15]  K. P. Soman,et al.  Secrecy of Cryptography with Compressed Sensing , 2012, 2012 International Conference on Advances in Computing and Communications.

[16]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[17]  Yi Cao,et al.  A parallel image encryption algorithm based on the piecewise linear chaotic map and hyper-chaotic map , 2018, Nonlinear Dynamics.