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An Image Compression-Encryption Algorithm Based on Compressed Sensing and Chaotic Oscillator

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Cybersecurity

Part of the book series: Studies in Big Data ((SBD,volume 102))

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Abstract

In this chapter, a chaotic oscillator is presented. Various dynamical behaviors of the oscillator are analyzed. The complex dynamics of the proposed oscillator are applied in a compression-encryption algorithm. Here, we propose an image compression-encryption method using compressed sensing and a chaotic oscillator. At first, the original image is represented in the wavelet domain to obtain sparse coefficients. The sparse representation is scrambled with a chaotic sequence. The scrambling operation increases the security level and improves the performance of sparse recovery in the decryption process. The sparse scrambled representation is then compressed using the chaotic dynamics. The compressed matrix is also scrambled to reduce the elements’ correlation. To obtain an unrecognizable encrypted image, the XOR operation is used. In the decryption process, the smoothed \({l}_{0}\) norm (SL0) algorithm decreases the complexity of calculations for image reconstruction. Wiener filter is used in sparse recovery based on SL0 to improve image reconstruction. The results of the presented method are satisfying in various compression ratios. Security analysis illustrates the effectiveness of our method.

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References

  1. Abd El-Latif, A.A., Abd-El-Atty, B., Amin, M., Iliyasu, A.M.:Quantum-inspired cascaded discrete-time quantum walks with induced chaotic dynamics and cryptographic applications. Sci. Rep. 10, 1–16 (2020)

    Google Scholar 

  2. Belazi, A., Abd El-Latif, A.A., Diaconu, A.-V., Rhouma, R., Belghith, S.:Chaos-based partial image encryption scheme based on linear fractional and lifting wavelet transforms. Opt. Lasers Eng. 88, 37–50 (2017)

    Google Scholar 

  3. Belazi, A., Abd El-Latif, A.A., Belghith, S.:A novel image encryption scheme based on substitution-permutation network and chaos, Signal Process. 128:155–170 (2016)

    Google Scholar 

  4. Pham, V.-T., Vaidyanathan, S., Volos, C., Jafari, S., Alsaadi, F.E.: Chaos in a simple snap system with only one nonlinearity, its adaptive control and real circuit design. Arch. Control Sci. 29 (2019)

    Google Scholar 

  5. Volos, C.K., Pham, V.-T., Nistazakis, H.E., Stouboulos, I.N.: A dream that has come true: chaos from a nonlinear circuit with a real memristor. Int. J. Bifurc. Chaos 30, 2030036 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  6. Nazarimehr, F., Pham, V.-T., Rajagopal, K., Alsaadi, F.E., Hayat, T., Jafari, S.: A new imprisoned strange attractor. Int. J. Bifurc. Chaos 29, 1950181 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  7. Pham, V.-T., Akgul, A., Volos, C., Jafari, S., Kapitaniak, T.: Dynamics and circuit realization of a no-equilibrium chaotic system with a boostable variable. AEU-Int. J. Electron. Commun. 78, 134–140 (2017)

    Article  Google Scholar 

  8. Pham, V.-T., Ouannas, A., Volos, C., Kapitaniak, T.: A simple fractional-order chaotic system without equilibrium and its synchronization. AEU-Int J Electron Commun 86, 69–76 (2018)

    Article  Google Scholar 

  9. Munoz-Pacheco, J.M., Zambrano-Serrano, E., Volos, C., Jafari, S., Kengne, J., Rajagopal, K.: A new fractional-order chaotic system with different families of hidden and self-excited attractors. Entropy 20, 564 (2018)

    Article  Google Scholar 

  10. Karthikeyan, A., Rajagopal, K., Kamdoum Tamba, V., Adam, G., Srinivasan, A.:A simple chaotic wien bridge oscillator with a fractional-order memristor and its combination synchronization for efficient antiattack capability. Complexity 2021 (2021)

    Google Scholar 

  11. Rajagopal, K., Singh, J.P., Karthikeyan, A., Roy, B.K.: Existence of metastable, hyperchaos, line of equilibria and self-excited attractors in a new hyperjerk oscillator. Int. J. Bifurc. Chaos 30, 2030037 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  12. Sambas, A., Vaidyanathan, S., Tlelo-Cuautle, E., Abd-El-Atty, B., Abd El-Latif, A.A., Guillén-Fernández, O., et al.: A 3-D multi-stable system with a peanut-shaped equilibrium curve: circuit design, FPGA realization, and an application to image encryption. IEEE Access 8, 137116–137132 (2020)

    Google Scholar 

  13. Jafari, S., Sprott, J., Nazarimehr, F.: Recent new examples of hidden attractors. Eur. Phys. J. Spec. Top. 224, 1469–1476 (2015)

    Article  Google Scholar 

  14. Wang, R., Li, C., Çiçek, S., Rajagopal, K., Zhang, X.:A Memristive hyperjerk chaotic system: amplitude control, FPGA design, and prediction with artificial neural network. Complexity 2021 (2021)

    Google Scholar 

  15. Zhang, X., Li, C., Chen, Y., Herbert, H., Lei, T.: A memristive chaotic oscillator with controllable amplitude and frequency. Chaos Solitons Fractals 139, 110000 (2020)

    Google Scholar 

  16. Sun, J., Li, C., Lu, T., Akgul, A., Min, F.: A memristive chaotic system with hypermultistability and its application in image encryption. IEEE Access 8, 139289–139298 (2020)

    Article  Google Scholar 

  17. Yu, Y., Shi, M., Kang, H., Chen, M., Bao, B.;Hidden dynamics in a fractional-order memristive Hindmarsh—Rose model. Nonlinear Dyn. 1–16 (2020)

    Google Scholar 

  18. Bao, H., Zhu, D., Liu, W., Xu, Q., Chen, M., Bao, B.: Memristor synapse-based morris-lecar model: bifurcation analyses and FPGA-based validations for periodic and chaotic bursting/spiking firings. Int. J. Bifurc. Chaos 30, 2050045 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  19. Bao, B., Peol, M., Bao, H., Chen, M., Li, H., Chen, B.: No-argument memristive hyper-jerk system and its coexisting chaotic bubbles boosted by initial conditions. Chaos Solitons Fractals 144, 110744 (2021)

    Google Scholar 

  20. Akgul, A., Kengne, J., Rajagopal K., Pham, V.-T., Varan, M., Karthikeyan, A., et al.: Simulation and experimental implementations of memcapacitor based multi-stable chaotic oscillator and its dynamical analysis. Phys. Script. 96, 015209 (2020)

    Google Scholar 

  21. Vaidyanathan, S., Tlelo-Cuautle, E., Anand, P.G., Sambas, A., Guillén-Fernández, O., Zhang, S.: A new conservative chaotic dynamical system with lemniscate equilibrium, its circuit model and FPGA implementation. Int. J. Autom. Control 15, 128–148 (2021)

    Article  Google Scholar 

  22. Vaidyanathan, S., Tlelo-Cuautle, E., Sambas, A., Dolvis, L.G., Guillén-Fernández, O.: FPGA design and circuit implementation of a new four-dimensional multistable hyperchaotic system with coexisting attractors. Int. J. Comput. Appl. Technol. 64, 223–234 (2020)

    Article  Google Scholar 

  23. Vaidyanathan, S., Tlelo-Cuautle, E., Sambas, A., Dolvis, L.G., Guillén-Fernández, O.: A new four-dimensional two-scroll hyperchaos dynamical system with no rest point, bifurcation analysis, multi-stability, circuit simulation and FPGA design. Int. J. Comput. Appl. Technol. 63, 147–159 (2020)

    Article  Google Scholar 

  24. Silva-Juárez, A., Tlelo-Cuautle, E., de la Fraga, L.G., Li, R.: Optimization of the Kaplan-Yorke dimension in fractional-order chaotic oscillators by metaheuristics. Appl. Math. Comput. 394, 125831 (2021)

    Google Scholar 

  25. Rajagopal, K., Kingni, S.T., Khalaf, A.J.M., Shekofteh, Y., Nazarimehr, F.: Coexistence of attractors in a simple chaotic oscillator with fractional-order-memristor component: analysis, FPGA implementation, chaos control and synchronization. Eur. Phys. J. Spec. Top. 228, 2035–2051 (2019)

    Article  Google Scholar 

  26. Rajagopal, K., Nazarimehr, F., Guessas, L., Karthikeyan, A., Srinivasan, A., Jafari, S.: Analysis, control and FPGA implementation of a fractional-order modified Shinriki circuit. J. Circ. Syst. Comput. 28, 1950232 (2019)

    Article  Google Scholar 

  27. Cao, W., Zhou, Y., Chen, C.P., Xia, L.: Medical image encryption using edge maps. Signal Process. 132, 96–109 (2017)

    Article  Google Scholar 

  28. Belazi, A., Talha, M., Kharbech, S., Xiang, W.: Novel medical image encryption scheme based on chaos and DNA encoding. IEEE access 7, 36667–36681 (2019)

    Article  Google Scholar 

  29. Hua, Z., Yi, S., Zhou, Y.: Medical image encryption using high-speed scrambling and pixel adaptive diffusion. Signal Process. 144, 134–144 (2018)

    Article  Google Scholar 

  30. Patel, K.D., Belani, S.: Image encryption using different techniques: a review. Int. J. Emerg. Technol. Adv. Eng. 1, 30–34 (2011)

    Google Scholar 

  31. Kaur, M., Kumar, V.: A comprehensive review on image encryption techniques. Arch. Comput. Methods Eng. 27, 15–43 (2020)

    Article  MathSciNet  Google Scholar 

  32. Chen, G., Mao, Y., Chui, C.K.: A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos Solitons Fractals 21, 749–761 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  33. Liu, H., Kadir, A.:Asymmetric color image encryption scheme using 2D discrete-time map. Signal Process. 113, 104–112 (2015)

    Google Scholar 

  34. Han, J., Park, C.-S., Ryu, D.-H., Kim, E.-S.: Optical image encryption based on XOR operations. Opt. Eng. 38, 47–54 (1999)

    Article  Google Scholar 

  35. Ahmad, J., Khan, M.A., Ahmed, F., Khan, J.S.: A novel image encryption scheme based on orthogonal matrix, skew tent map, and XOR operation. Neural Comput. Appl. 30, 3847–3857 (2018)

    Article  Google Scholar 

  36. Zhou, R.-G., Wu, Q., Zhang, M.-Q., Shen, C.-Y.: Quantum image encryption and decryption algorithms based on quantum image geometric transformations. Int. J. Theor. Phys. 52, 1802–1817 (2013)

    Article  MathSciNet  Google Scholar 

  37. Zhou, N.R., Hua, T.X., Gong, L.H., Pei, D.J., Liao, Q.H.: Quantum image encryption based on generalized Arnold transform and double random-phase encoding. Quantum Inf. Process. 14, 1193–1213 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  38. Zhang, Q., Guo, L., Wei, X.: Image encryption using DNA addition combining with chaotic maps. Math. Comput. Model. 52, 2028–2035 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  39. Wu, J., Liao, X., Yang, B.: Image encryption using 2D Hénon-Sine map and DNA approach. Signal Process. 153, 11–23 (2018)

    Article  Google Scholar 

  40. Telem, A.N., Fotsin, H.B., Kengne, J.: Image encryption algorithm based on dynamic DNA coding operations and 3D chaotic systems. Multimed. Tools Appl. 1–31 (2021)

    Google Scholar 

  41. Farah, M.B., Guesmi, A. Kachouri, R., Samet, M.: A novel chaos based optical image encryption using fractional Fourier transform and DNA sequence operation. Opt. Laser Technol. 121, 105777 (2020)

    Google Scholar 

  42. Refregier, P., Javidi, B.: Optical image encryption based on input plane and fourier plane random encoding. Opt. Lett. 20, 767–769 (1995)

    Article  Google Scholar 

  43. Liu, S., Guo, C., Sheridan, J.T.: A review of optical image encryption techniques. Opt. Laser Technol. 57, 327–342 (2014)

    Article  Google Scholar 

  44. Moysis, L., Volos, C., Jafari, S., Munoz-Pacheco, J.M., Kengne, J., Rajagopal, K., et al.: Modification of the logistic map using fuzzy numbers with application to pseudorandom number generation and image encryption. Entropy 22, 474 (2020)

    Article  MathSciNet  Google Scholar 

  45. Tsafack, N., Sankar, S., Abd-El-Atty, B., Kengne, J., Jithin, K., Belazi, A., et al.: A new chaotic map with dynamic analysis and encryption application in internet of health things. IEEE Access 8, 137731–137744 (2020)

    Article  Google Scholar 

  46. Sambas, A., Vaidyanathan, S., Tlelo-Cuautle, E., Abd-El-Atty, B., El-Latif, A.A.A., Guillén-Fernández, O., et al.: A 3-D multi-stable system with a peanut-shaped equilibrium curve: circuit design, FPGA realization, and an application to image encryption. IEEE Access 8, 137116–137132 (2020)

    Article  Google Scholar 

  47. Tsafack, N., Kengne, J., Abd-El-Atty, B., Iliyasu, A.M., Hirota, K., Abd El-Latif, A.A.: Design and implementation of a simple dynamical 4-D chaotic circuit with applications in image encryption. Inf. Sci. 515, 191–217 (2020)

    Google Scholar 

  48. Ghaffari, A.: Image compression-encryption method based on two-dimensional sparse recovery and chaotic system. Sci. Rep. 11, 1–19 (2021)

    Article  Google Scholar 

  49. Alanezi, A., Abd-El-Atty, B., Kolivand, H., El-Latif, A., Ahmed, A., El-Rahiem, A., et al.: Securing digital images through simple permutation-substitution mechanism in cloud-based smart city environment. Secur. Commun. Netw. 2021 (2021)

    Google Scholar 

  50. Wang, H., Xiao, D., Li, M., Xiang, Y., Li, X.: A visually secure image encryption scheme based on parallel compressive sensing. Signal Process. 155, 218–232 (2019)

    Article  Google Scholar 

  51. Wen, W., Hong, Y., Fang, Y., Li M., Li, M..: A visually secure image encryption scheme based on semi-tensor product compressed sensing. Signal Process. 173, 107580 (2020)

    Google Scholar 

  52. Yang, Y.-G., Wang, B.-P., Yang, Y.-L., Zhou, Y.-H., Shi, W.-M., Liao, X.: Visually meaningful image encryption based on universal embedding model. Inf. Sci. 562, 304–324 (2021)

    Article  MathSciNet  Google Scholar 

  53. Xu, Q., Sun, K., He, S., Zhu, C.: An effective image encryption algorithm based on compressive sensing and 2D-SLIM. Opt. Lasers Eng. 134, 106178 (2020)

    Google Scholar 

  54. Hua, Z., Zhang, K. Li, Y. Zhou, Y.: Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing. Signal Process. 183, 107998 (2021)

    Google Scholar 

  55. Liu, X., Cao, Y., Lu, P., Lu, X., Li, Y.: Optical image encryption technique based on compressed sensing and Arnold transformation. Optik 124, 6590–6593 (2013)

    Article  Google Scholar 

  56. Zhou, N., Jiang, H., Gong, L., Xie, X.: Double-image compression and encryption algorithm based on co-sparse representation and random pixel exchanging. Opt. Lasers Eng. 110, 72–79 (2018)

    Article  Google Scholar 

  57. Hu, G., Xiao, D., Wang, Y., Xiang, T.: An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J. Vis. Commun. Image Represent. 44, 116–127 (2017)

    Article  Google Scholar 

  58. Zhu, L., Song, H., Zhang, X., Yan, M., Zhang, L., Yan, T.: A novel image encryption scheme based on nonuniform sampling in block compressive sensing. IEEE Access 7, 22161–22174 (2019)

    Article  Google Scholar 

  59. Chen, T., Zhang, M., Wu, J., Yuen, C., Tong, Y.: Image encryption and compression based on kronecker compressed sensing and elementary cellular automata scrambling. Opt. Laser Technol. 84, 118–133 (2016)

    Article  Google Scholar 

  60. Zhang, Y., Zhou, J., Chen, F., Zhang, L.Y., Wong, K.-W., He, X., et al.: Embedding cryptographic features in compressive sensing. Neurocomputing 205, 472–480 (2016)

    Article  Google Scholar 

  61. Hu, H., Cao, Y., Xu, J., Ma, C., Yan, H.: An image compression and encryption algorithm based on the fractional-order simplest chaotic circuit. IEEE Access 9, 22141–22155 (2021)

    Article  Google Scholar 

  62. Chen, J., Zhang, Y., Qi, L., Fu, C., Xu, L.: Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression. Opt. Laser Technol. 99, 238–248 (2018)

    Article  Google Scholar 

  63. Chai, X., Zheng, X., Gan, Z., Han, D., Chen, Y.: An image encryption algorithm based on chaotic system and compressive sensing. Signal Process. 148, 124–144 (2018)

    Article  Google Scholar 

  64. Sun, C., Wang, E., Zhao, B.: Image encryption scheme with compressed sensing based on a new six-dimensional non-degenerate discrete hyperchaotic system and plaintext-related scrambling. Entropy 23, 291 (2021)

    Article  MathSciNet  Google Scholar 

  65. Fan, J.-H., Liu, X.-B., Chen, Y.-B.: Image compression and encryption algorithm with wavelet-transform-based 2D compressive sensing. Opt. Appl. 49 (2019)

    Google Scholar 

  66. Zhou, N., Pan, S., Cheng, S., Zhou, Z.: Image compression—Encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt. Laser Technol. 82, 121–133 (2016)

    Article  Google Scholar 

  67. Deng, J., Zhao, S., Wang, Y., Wang, L., Wang, H., Sha, H.: Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform. Multimed. Tools Appl. 76, 10097–10117 (2017)

    Article  Google Scholar 

  68. Ye, G., Pan, C., Dong, Y., Shi, Y., Huang, X.: Image encryption and hiding algorithm based on compressive sensing and random numbers insertion. Signal Process. 107563 (2020)

    Google Scholar 

  69. Gong, L., Deng, C., Pan, S., Zhou, N.: Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform. Opt. Laser Technol. 103, 48–58 (2018)

    Article  Google Scholar 

  70. Chai, X., Fu, X., Gan, Z., Zhang, Y., Lu, Y., Chen, Y.: An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata. Neural Comput. Appl. 32, 4961–4988 (2020)

    Article  Google Scholar 

  71. Chai, X., Gan, Z., Chen, Y., Zhang, Y.: A visually secure image encryption scheme based on compressive sensing. Signal Process. 134, 35–51 (2017)

    Article  Google Scholar 

  72. Ghaffari, A., Babaie-Zadeh, M., Jutten, C.: Sparse decomposition of two dimensional signals. In: 2009 IEEE international conference on acoustics, speech and signal processing, pp. 3157–3160 (2009)

    Google Scholar 

  73. Duarte, M.F., Baraniuk, R.G.: Kronecker compressive sensing. IEEE Trans. Image Process. 21, 494–504 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  74. Jokar, S., Mehrmann, V.: Sparse solutions to underdetermined Kronecker product systems. Linear Algebra Appl. 431, 2437–2447 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  75. Mohimani, H., Babaie-Zadeh, M., Jutten, C.: A fast approach for overcomplete sparse decomposition based on smoothed ℓ0 norm. IEEE Trans. Signal Process. 57, 289–301 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  76. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  77. Eldar, Y.C., Kutyniok, G.: Compressed sensing: theory and applications: Cambridge university press (2012)

    Google Scholar 

  78. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2008)

    Article  Google Scholar 

  79. Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition?. In: 2011 international conference on computer vision, pp 471–478 (2011)

    Google Scholar 

  80. Ghaffari, A., Fatemizadeh, E.: Sparse-induced similarity measure: mono-modal image registration via sparse-induced similarity measure. IET Image Proc. 8, 728–741 (2014)

    Article  MATH  Google Scholar 

  81. Ghaffari, A., Fatemizadeh, E.: Robust Huber similarity measure for image registration in the presence of spatially-varying intensity distortion. Signal Process. 109, 54–68 (2015)

    Article  Google Scholar 

  82. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)

    Article  MATH  Google Scholar 

  83. Zhang, Z., Chen, X., Liu, L., Li, Y., Deng, Y.: A sparse representation denoising algorithm for visible and infrared image based on orthogonal matching pursuit. SIViP 14, 737–745 (2020)

    Article  Google Scholar 

  84. Gribonval, R., Nielsen, M.: Sparse decompositions in unions of bases. IEEE Trans. Inform. Theory 49, 3320–3325 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  85. Eftekhari, A., Babaie-Zadeh, M., Moghaddam, H.A.: Two-dimensional random projection. Signal Process. 91, 1589–1603 (2011)

    Article  MATH  Google Scholar 

  86. Fang, Y., Wu, J., Huang, B.: 2D sparse signal recovery via 2D orthogonal matching pursuit. Sci. China Inf. Sci. 55, 889–897 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  87. Chen, G., Li, D., Zhang, J.: Iterative gradient projection algorithm for two-dimensional compressive sensing sparse image reconstruction. Signal Process. 104, 15–26 (2014)

    Article  Google Scholar 

  88. Blake, A., Zisserman, A.: Visual reconstruction. MIT press (1987)

    Google Scholar 

  89. Eftekhari, A., Babaie-Zadeh, M., Jutten, C., Moghaddam, H.A.: Robust-SL0 for stable sparse representation in noisy settings. In: 2009 IEEE international conference on acoustics, speech and signal processing, pp. 3433–3436 (2009)

    Google Scholar 

  90. Zhu, S., Zhu, C., Wang, W.: A novel image compression-encryption scheme based on chaos and compression sensing. IEEE Access 6, 67095–67107 (2018)

    Article  Google Scholar 

  91. Alvarez, G., Li, S.: Some basic cryptographic requirements for chaos-based cryptosystems. Int. J. Bifurc. Chaos 16, 2129–2151 (2006)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    Aboozar Ghaffari

  2. Department of Biomedical Engineering, Amirkabir University of Technology (Tehran polytechnic), Tehran, Iran

    Fahimeh Nazarimehr & Sajad Jafari

  3. Health Technology Research Institute, Amirkabir University of Technology (Tehran polytechnic), Tehran, Iran

    Sajad Jafari

  4. Department of Electronics, Instituto Nacional de Astrofísica, Óptica Y Electrónica (INAOE), Tonantzintla, Puebla, 72840, México

    Esteban Tlelo-Cuautle

Authors
  1. Aboozar Ghaffari
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  2. Fahimeh Nazarimehr
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  3. Sajad Jafari
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  4. Esteban Tlelo-Cuautle
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Corresponding author

Correspondence to Aboozar Ghaffari .

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Editors and Affiliations

  1. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shibin El Kom, Egypt

    Ahmed A. Abd El-Latif

  2. Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Christos Volos

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Ghaffari, A., Nazarimehr, F., Jafari, S., Tlelo-Cuautle, E. (2022). An Image Compression-Encryption Algorithm Based on Compressed Sensing and Chaotic Oscillator. In: Abd El-Latif, A.A., Volos, C. (eds) Cybersecurity. Studies in Big Data, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-030-92166-8_2

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