An image compression and encryption algorithm based on chaotic system and compressive sensing

Abstract For a linear image encryption system, it is vulnerable to the chosen-plaintext attack. To overcome the weakness and reduce the correlation among pixels of the encryption image, an effective image compression and encryption algorithm based on chaotic system and compressive sensing is proposed. The original image is first permuted by the Arnold transform to reduce the block effect in the compression process, and then the resulting image is compressed and re-encrypted by compressive sensing, simultaneously. Moreover, the bitwise XOR operation based on chaotic system is performed on the measurements to change the pixel values and a pixel scrambling method is employed to disturb the positions of pixels. Besides, the keys used in chaotic systems are related to the plaintext image. Simulation results verify the effectiveness and reliability of the proposed image compression and encryption algorithm with considerable compression and security performance.

[1]  Wenqi He,et al.  Multiple-image encryption via lifting wavelet transform and XOR operation based on compressive ghost imaging scheme , 2018 .

[2]  Yong Zhang,et al.  The image encryption algorithm with plaintext-related shuffling , 2016 .

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

[4]  S. Agaian,et al.  NPCR and UACI Randomness Tests for Image Encryption , 2011 .

[5]  Di Wang,et al.  Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform , 2015 .

[6]  Tao Xiang,et al.  Securing image information using double random phase encoding and parallel compressive sensing with updated sampling processes , 2017 .

[7]  Sos S. Agaian,et al.  Local Shannon entropy measure with statistical tests for image randomness , 2013, Inf. Sci..

[8]  Yiran Chen,et al.  A visually secure image encryption scheme based on compressive sensing , 2017, Signal Process..

[9]  Chengzhi Deng,et al.  Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform , 2018, Optics & Laser Technology.

[10]  X. Liao,et al.  An image encryption approach based on chaotic maps , 2005 .

[11]  Huiqian Du,et al.  Simultaneous image compression, fusion and encryption algorithm based on compressive sensing and chaos , 2016 .

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

[13]  Xiang Peng,et al.  Multiple-Image Encryption Based on Compressive Ghost Imaging and Coordinate Sampling , 2016, IEEE Photonics Journal.

[14]  Zhiliang Zhu,et al.  A fast image encryption scheme with a novel pixel swapping-based confusion approach , 2014, Nonlinear Dynamics.

[15]  Mohamed A. Deriche,et al.  A new wavelet based efficient image compression algorithm using compressive sensing , 2015, Multimedia Tools and Applications.

[16]  Rong Li,et al.  Compressive optical image encryption with two-step-only quadrature phase-shifting digital holography , 2015 .

[17]  Yuanyuan Zhou,et al.  Multiple-image encryption based on double random phase encoding and compressive sensing by using a measurement array preprocessed with orthogonal-basis matrices , 2018, Optics & Laser Technology.

[18]  Yicong Zhou,et al.  Medical image encryption using high-speed scrambling and pixel adaptive diffusion , 2018, Signal Process..

[19]  Linfei Chen,et al.  Optical image conversion and encryption by diffraction, phase retrieval algorithm and incoherent superposition , 2017 .

[20]  Hao Jiang,et al.  Double-image compression and encryption algorithm based on co-sparse representation and random pixel exchanging , 2018, Optics and Lasers in Engineering.

[21]  Hong Wang,et al.  Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform , 2017, Multimedia Tools and Applications.

[22]  Di Xiao,et al.  An image encryption scheme based on rotation matrix bit-level permutation and block diffusion , 2014, Commun. Nonlinear Sci. Numer. Simul..

[23]  J. Fridrich.  Symmetric Ciphers Based on Two-Dimensional Chaotic Maps , 1998 .

[24]  Rengarajan Amirtharajan,et al.  Chaos based crossover and mutation for securing DICOM image , 2016, Comput. Biol. Medicine.

[25]  Jianhua Wu,et al.  Image encryption scheme based on random fractional discrete cosine transform and dependent scrambling and diffusion , 2017 .

[26]  Olcay Taner Yildiz,et al.  Cryptanalysis of Fridrich's Chaotic Image Encryption , 2010, Int. J. Bifurc. Chaos.

[27]  Abdul Hanan Abdullah,et al.  Image encryption using a synchronous permutation-diffusion technique , 2017 .

[28]  Zhihong Zhou,et al.  Optical multi-image encryption scheme based on discrete cosine transform and nonlinear fractional Mellin transform , 2015, Multimedia Tools and Applications.

[29]  D. Ravichandran,et al.  DNA Chaos Blend to Secure Medical Privacy , 2017, IEEE Transactions on NanoBioscience.

[30]  Wenqi He,et al.  Two-level image authentication by two-step phase-shifting interferometry and compressive sensing , 2018 .

[31]  Ding Wei,et al.  DIGITAL IMAGE TRANSFORMATION AND INFORMATION HIDING AND DISGUISING TECHNOLOGY , 1998 .

[32]  Hejiao Huang,et al.  2D Logistic-Sine-coupling map for image encryption , 2018, Signal Process..

[33]  Xiaofeng Liao,et al.  A novel scheme for simultaneous image compression and encryption based on wavelet packet transform and multi-chaotic systems , 2018, Multimedia Tools and Applications.