Component Fusion Image Encryption Method Based on Hybrid Chaotic Model

Shuiqing Xiao; Songbo Wang; Caihong Ye; Yuan Gao

doi:10.20965/jaciii.2019.p0119

single-jc.php

« previous

JACIII Vol.23 No.1 pp. 119-123

doi: 10.20965/jaciii.2019.p0119

(2019)

Short Paper:

Views over last 60 days: 33

Component Fusion Image Encryption Method Based on Hybrid Chaotic Model

Shuiqing Xiao, Songbo Wang^†, Caihong Ye, and Yuan Gao

Business School, Lingnan Normal University
Zhanjiang, Guangdong 524048, China

^†Corresponding author

Received:

May 29, 2018

Accepted:

July 6, 2018

Published:

January 20, 2019

Keywords:

hybrid chaotic model, component fusion image, encryption method

Abstract

Component Fusion Image Encryption Method Based on Hybrid Chaotic Model

The encrypted image

The current image encryption method is relatively simple, and there is the problem of poor image encryption effect. Based on the hybrid chaotic model, an image encryption method with component fusion is proposed in this paper. The image is mapped by using Arnold cat mapping method. Chaotic sequence is generated by chaotic model, and the original image is scrambled and substituted to achieve image encryption. Through the fixed point ratio, information entropy, gray mean change value, autocorrelation and similarity, the test of encrypted image is completed. Experimental results show that the proposed image encryption method has good performance, high security intensity, and can effectively encrypt the image.

Cite this article as:

S. Xiao, S. Wang, C. Ye, and Y. Gao, “Component Fusion Image Encryption Method Based on Hybrid Chaotic Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.1, pp. 119-123, 2019.

Data files:

References

[1] Z. Hua and Y. Zhou, “Image encryption using 2D Logistic-adjusted-Sine map,” Information Sciences, Vol.339, No.C, pp. 237-253, 2016.
[2] H. Zhou, “Classification of Large Data Privacy Encryption Simulation Research,” Computer Simulation, Vol.33, No.7, pp. 414-417, 2016.
[3] C. Li, T. Xie, Q. Liu, et al., “Cryptanalyzing image encryption using chaotic logistic map,” Nonlinear Dynamics, Vol.78, No.2, pp. 1545-1551, 2016.
[4] X. Wu, D. Wang, and H. Kan, “A novel lossless color image encryption scheme using 2D DWT and 6D hyperchaotic system,” Information Sciences, Vol.349, No.C, pp. 137-153, 2016.
[5] X. Wang and H.-L. Zhang, “A novel image encryption algorithm based on genetic recombination and hyper-chaotic systems,” Nonlinear Dynamics, Vol.83, Issue 1-2, pp. 333-346, 2016.
[6] W. Wang, C. Liu, and H. Zhang, “An effective and fast image encryption algorithm based on Chaos and interweaving of ranks,” Nonlinear Dynamics, Vol.84, No.3, pp. 1595-1607, 2016.
[7] G. Yang, Y. Zhang, J. Yang, et al., “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools & Applications, Vol.75, No.23, pp. 15601-15617, 2016.
[8] G. Yang, W. Tan, H. Jin, et al., “Review wearable sensing system for gait recognition,” Cluster Computing, pp. 1-9, 2018.

[1] [1] Z. Hua and Y. Zhou, “Image encryption using 2D Logistic-adjusted-Sine map,” Information Sciences, Vol.339, No.C, pp. 237-253, 2016.

[2] [2] H. Zhou, “Classification of Large Data Privacy Encryption Simulation Research,” Computer Simulation, Vol.33, No.7, pp. 414-417, 2016.

[3] [3] C. Li, T. Xie, Q. Liu, et al., “Cryptanalyzing image encryption using chaotic logistic map,” Nonlinear Dynamics, Vol.78, No.2, pp. 1545-1551, 2016.

[4] [4] X. Wu, D. Wang, and H. Kan, “A novel lossless color image encryption scheme using 2D DWT and 6D hyperchaotic system,” Information Sciences, Vol.349, No.C, pp. 137-153, 2016.

[5] [5] X. Wang and H.-L. Zhang, “A novel image encryption algorithm based on genetic recombination and hyper-chaotic systems,” Nonlinear Dynamics, Vol.83, Issue 1-2, pp. 333-346, 2016.

[6] [6] W. Wang, C. Liu, and H. Zhang, “An effective and fast image encryption algorithm based on Chaos and interweaving of ranks,” Nonlinear Dynamics, Vol.84, No.3, pp. 1595-1607, 2016.

[7] [7] G. Yang, Y. Zhang, J. Yang, et al., “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools & Applications, Vol.75, No.23, pp. 15601-15617, 2016.

[8] [8] G. Yang, W. Tan, H. Jin, et al., “Review wearable sensing system for gait recognition,” Cluster Computing, pp. 1-9, 2018.

Component Fusion Image Encryption Method Based on Hybrid Chaotic Model

Shuiqing Xiao, Songbo Wang†, Caihong Ye, and Yuan Gao

Shuiqing Xiao, Songbo Wang^†, Caihong Ye, and Yuan Gao