Novel image encryption algorithm based TLM hyperchaotic cellular neural network

Di Xiaoqiang; Mu Yining; Li Jinqing; Yang Huamin

Since chaos is sensitive for initial values, it is very suitable for data encryption. An image encryption algorithm based on hyper-chaotic control parameters and mixed scrambling diffusion structure of higher -order chaotic system was presented. The encryption algorithm included scrambling step and diffusion step. In the scrambling step, the composite chaotic map was used to generate the alignment phase control parameters and scramble for the high-level image cross -correlation between the adjacent pixels. In the diffusion step, the composite chaotic map with the different initial states and parameters was used to generate the initial conditions for hyper-chaotic cellular neural networks in order to generate the key stream. This method was evaluated by known plaintext attack and chosen plaintext attack, key space, image histogram, and simulations show good results. Compared with several other related algorithms, it has better anti -aggressive and key sensitivity is high. It can be applied to the image encryption.

1. School of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,China

Received Date: 2014-04-10

Rev Recd Date: 2014-05-17

Publish Date: 2014-12-25

Key words:

Abstract: Since chaos is sensitive for initial values, it is very suitable for data encryption. An image encryption algorithm based on hyper-chaotic control parameters and mixed scrambling diffusion structure of higher -order chaotic system was presented. The encryption algorithm included scrambling step and diffusion step. In the scrambling step, the composite chaotic map was used to generate the alignment phase control parameters and scramble for the high-level image cross -correlation between the adjacent pixels. In the diffusion step, the composite chaotic map with the different initial states and parameters was used to generate the initial conditions for hyper-chaotic cellular neural networks in order to generate the key stream. This method was evaluated by known plaintext attack and chosen plaintext attack, key space, image histogram, and simulations show good results. Compared with several other related algorithms, it has better anti -aggressive and key sensitivity is high. It can be applied to the image encryption.

HTML

Reference (29)

[1]	Shannon C E. Communication theory of secrecy systems[J]. Bell System Technical Journal, 1949, 28(4): 656-715.
[2]
[3]
[4]	Yan Huang, Yang Xiaosong. Hyperchaos and bifurcation in a new class of four-dimensional Hopfield neural networks [J]. Neurocomputing, 2006, 69(13-15): 1787-1795.
[5]	Fang Jingyue, Zhou Pu, Kang Qiang. Encrypting optical image based on compound encoding on space-fractional domain [J]. Infrared and Laser Engineering, 2005, 34(3): 345. (in Chinese)
[6]
[7]
[8]	Li Qingdu, Yang Xiaosong, Yang Fangyan. Hyperchaos in a simple CNN [J]. International Journal of Bifurcation and Chaos, 2006, 16(8): 2453-2457.
[9]
[10]	Gao Tiegang, Gu Qiaolun, Emmanuel Sabu. A novel image authentication scheme based on hyper-chaotic cell neural network[J]. Chaos, Solitons Fractals, 2009, 42(1): 548-553.
[11]	Li Jinqing, Bai Fengming, Di Xiaoqiang. Color image encryption algorithm based on hopfield chaotic neural networks [J]. Journal of Changchun University of Science and Technology, 2012, 35(4): 117-121. (in Chinese)
[12]
[13]
[14]	Li Jinqin, Bai Fengming, Di Xiaoqiang. New color image encryption algorithm based on compound chaos mapping and hyperchaotic cellular neural network[J]. Journal of Electronic Imaging, 2013, 22(1): 013036-013036. (in Chinese)
[15]
[16]	Liu Li, Zhou Yajian, Zhang Bin. Digital watermarking method for QR code images based on DCT and SVD [J]. Infrared and Laser Engineering, 2013, 42(z2): 304-311. (in Chinese)
[17]
[18]	Bigdeli Nooshin, Farid Yousef, Afshar Karim. A robust hybrid method for image encryption based on Hopfield neural network [J]. Computers Electrical Engineering, 2012, 38 (2): 356-369.
[19]	Ren Xiaoxia, Liao Xiaofeng, Xiong Yonghong. New image encryption algorithm based on cellular neural network [J]. Journal of Computer Applications, 2011, 31(6): 1528-1530.
[20]
[21]	Bai Fengming. Opto-electronic hybrid system chaos control, synchronization and applied research[D]. Changchun: Changchun University of Science and Technology, 2003. (in Chinese)
[22]
[23]	Lian S. A block cipher based on chaotic neural networks[J]. Neurocomputing, 2009, 72(4): 1296-1301.
[24]
[25]	Liu Hongjun, Wang Xingyuan. Color image encryption based on one-time keys and robust chaotic maps[J]. Computers Mathematics with Applications, 2010, 59(10): 3320-3327.
[26]
[27]
[28]	Rhouma R, Meherzi S, Belghith S. OCML-based colour image encryption [J]. Chaos, Solitons Fractals, 2009, 40 (1): 309-318.
[29]	Zeghid M, Machhout M, Khriji L, et al. A modified AES based algorithm for image encryption [J]. International Journal of Computer Science and Engineering, 2007, 1(1): 70-75.

Novel image encryption algorithm based TLM hyperchaotic cellular neural network

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views