Multimedia Tools and Applications

, Volume 77, Issue 11, pp 14285–14304 | Cite as

A hybrid scheme for self-adaptive double color-image encryption

  • Fang Han
  • Xiaofeng LiaoEmail author
  • Bo Yang
  • Yushu Zhang


Most of existing optical color image encryption schemes have born security risks due to the adoption of linear transform, and data redundancy for the generation of complex image. To settle these problems effectively, a hybrid scheme for self-adaptive double color-image encryption is proposed in this paper. In this scheme, each RGB color component of two secret color images is first compressed and encrypted by 2-D compressive sensing (CS) in which measurement matrices are generated by 1-D compound chaotic systems and further are optimized by steepest descent algorithm to improve image reconstruction effect. Then, the two measured images are regarded as the real part and imaginary part to constitute a complex image, respectively. In the end, the complex image is reencrypted by self-adaptive random phase encoding and discrete fractional random transform (DFrRT) to obtain the final encrypted data. In the process of DFrRT and random phase encoding, the correlations between R, G, B components are adequately utilized. The production of key streams not only depends on the initial values of chaotic systems but also on plaintexts, and the three color components affect each other to enhance the ability against the known plaintext attack. The projection neural network algorithm is adopted to obtain the decryption images. Simulation results also verify the validity and security of the proposed method.


Self-adaptive Compressive sensing Fractional random transform Image encryption Compound chaotic systems 



This work was supported in part by the National Key Research and Development Program of China under Grant 2016 YFB0800601, in part by the National Nature Science Foundation of China under Grant 61472331, in part by the Talents of Science and Technology promote plan, Chongqing Science & Technology Commission.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information EngineeringSouthwest UniversityChongqingChina

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