Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8759–8783 | Cite as

A novel image encryption algorithm based on LFT based S-boxes and chaos

  • Zhihua Gan
  • Xiuli Chai
  • Ke Yuan
  • Yang Lu


A novel and efficient image encryption algorithm based on the chaotic system and S-boxes is introduced in this paper, in which an original S-box is produced by linear fractional transformation (LFT) on Galois field of order 256, and then a set of S-boxes are obtained by performing zigzag confusion on the original S-box. The encryption architecture of forward substitution process (FSP) and reverse substitution process (RSP) is adopted. For each pixel of the plain image, a corresponding element in a certain S-box is chosen, and the choosing process of the S-box and element depends on two random numbers, the plain image pixel and the previous cipher pixel. Moreover, 2D–LASM is used to generate the random numbers, and its initial values and system parameter are computed by the SHA 256 hash of the plain image and the given values. Therefore, the proposed scheme has highly relationship with the original image and it can resist known-plaintext and chosen-plaintext attacks. Besides, correlated chaos and correlated substitution are used to improve the security level. Experiment results and security analyses demonstrate that the proposed image encryption algorithm is secure and efficient.


Image encryption S-box Forward substitution process (FSP) Reverse substitution process (RSP) Sha 256 Chaos 



All the authors are deeply grateful to the editors for careful and fast handling of the manuscript. The authors would also like to thank the anonymous referees for their valuable suggestions to improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (Grant No. 41571417 and U1604145), National Science Foundation of the United States (Grant No. CNS-1253424 and ECCS-1202225), Science and Technology Foundation of Henan Province of China (Grant No. 152102210048), Foundation and Frontier Project of Henan Province of China (Grant No. 162300410196), China Postdoctoral Science Foundation (Grant No. 2016M602235), Natural Science Foundation of Educational Committee of Henan Province of China (Grant No. 14A413015), the Research Foundation of Henan University (Grant No. xxjc20140006) and Henan Postdoctoral Scientific Program.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of SoftwareHenan UniversityKaifengChina
  2. 2.Institute of Image Processing and Pattern Recognition, School of Computer and Information EngineeringHenan UniversityKaifengChina
  3. 3.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA
  4. 4.Research DepartmentHenan UniversityKaifengChina

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