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A new image compression-encryption scheme based on compressive sensing & classical AES algorithm

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Abstract

In recent years, many compressive sensing methods have been suggested to encrypt and compress images. However, these algorithms have some flaws in terms of the quality of the reconstructed images, compression ratio value, security performance, and encryption speed. Therefore, in this paper, a new image compression-encryption scheme is proposed based on compressive sensing and the AES-128 algorithm. The sparse coefficients are permuted by a matrix produced each time by the initial variable of the 6D hyperchaotic system to enhance the compression performance of compressive sensing. Additionally, the 6D hyperchaotic system uses two variables to generate the measurement matrix for compressive sensing. Moreover, to increase the security level of the proposed algorithm, the AES algorithm (using ECB mode) is applied to the compressed image where the AES input key is generated by two variables the 6D hyperchaotic system, and each column has its own input key. Experimental and analysis results show that the proposed algorithm has good performance in terms of security, such as large key-space, high sensitivity, statistical attacks, and good compression performance compared to existing algorithms.

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References

  1. Abbasi AA, Mazinani M, Hosseini R (2020) Chaotic evolutionary-based image encryption using RNA codons and amino acid truth table. Opt Laser Technol 132:106465. https://doi.org/10.1016/j.optlastec.2020.106465

    Article  Google Scholar 

  2. Ajagbe SA, Adesina AO (2020) Design and Development of an Access Control Based Electronic Medical Records (EMR). Centrepoint J (Sci Ed) 26(1):98–119

    Google Scholar 

  3. Ajagbe SA, Adesina AO, Odule TJ, Aiyeniko O (2020) Evaluation of computing resources consumption of selected symmetric-key algorithms. J Comput Sci Appl 26(2):64. https://doi.org/10.4314/jcsia.v26i2.7

    Article  Google Scholar 

  4. Alvarez G, Li S (2006) Some basic cryptographic requirements for chaos-based cryptosystems. Int J Bifurcat Chaos 16(08):2129–2151. https://doi.org/10.1142/S0218127406015970

    Article  MathSciNet  MATH  Google Scholar 

  5. Bao Y, Tang Z, Li H (2020) Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach. Struct Health Monit 19(1):293–304. https://doi.org/10.1177/1475921719844039

    Article  Google Scholar 

  6. Candes EJ, Tao T (2005) Decoding by Linear Programming. IEEE Trans Inf Theory 51(12):4203–4215. https://doi.org/10.1109/TIT.2005.858979

    Article  MathSciNet  MATH  Google Scholar 

  7. Candès E, Romberg J, Tao T (2006) Robust uncertainty principles : exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  MathSciNet  MATH  Google Scholar 

  8. Chai X, Zheng X, Gan Z, Han D, Chen Y (2018) An image encryption algorithm based on chaotic system and compressive sensing. Signal Process 148:124–144. https://doi.org/10.1016/j.sigpro.2018.02.007

    Article  Google Scholar 

  9. Dai W, Milenkovic O (2009) Subspace Pursuit for Compressive Sensing Signal Reconstruction. IEEE Trans Inf Theory 55(5):2230–2249. https://doi.org/10.1109/TIT.2009.2016006

    Article  MathSciNet  MATH  Google Scholar 

  10. Donoho DL, Elad M, Temlyakov VN (2006) Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inf Theory 52(1):6–18. https://doi.org/10.1109/TIT.2005.860430

    Article  MathSciNet  MATH  Google Scholar 

  11. Fang H, Vorobyov SA, Jiang H, Taheri O (2014) Permutation Meets Parallel Compressed Sensing: How to Relax Restricted Isometry Property for 2D Sparse Signals. IEEE Trans Signal Process 62(1):196–210. https://doi.org/10.1109/TSP.2013.2284762

    Article  MathSciNet  MATH  Google Scholar 

  12. Fei L, Yan L, Chen C, Ye Z, Zhou J (2017) OSSIM: An Object-Based Multiview Stereo Algorithm Using SSIM Index Matching Cost. IEEE Trans Geosci Remote Sens 55(12):6937–6949. https://doi.org/10.1109/TGRS.2017.2737033

    Article  Google Scholar 

  13. Gan Z, Chai X, Zhang J, Zhang Y, Chen Y (2020) An effective image compression–encryption scheme based on compressive sensing (CS) and game of life (GOL). Neural Comput & Applic 32(17):14113–14141. https://doi.org/10.1007/s00521-020-04808-8

    Article  Google Scholar 

  14. Hadj Brahim A, Ali Pacha A, Hadj Said N (2020) Image encryption based on compressive sensing and chaos systems. Opt Laser Technol 132:106489. https://doi.org/10.1016/j.optlastec.2020.106489

    Article  Google Scholar 

  15. Hadj Brahim A, Ali Pacha A, Hadj Said N (2021) A new image encryption scheme based on a hyperchaotic system & multi specific S-boxes. Inf Secur J: A Global Perspective 32:59–75. https://doi.org/10.1080/19393555.2021.1943572

    Article  Google Scholar 

  16. Han C (2019) An image encryption algorithm based on modified logistic chaotic map. Optik. 181:779–785. https://doi.org/10.1016/j.ijleo.2018.12.178

    Article  Google Scholar 

  17. Hore A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. 2010 20th international conference on pattern recognition (Istanbul, Turkey, Aug. 2010), 2366–2369

  18. Hu G, Xiao D, Wang Y, Xiang T (2017) An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J Vis Commun Image Represent 44:116–127. https://doi.org/10.1016/j.jvcir.2017.01.022

    Article  Google Scholar 

  19. Khan JS, Kayhan SK (2021) Chaos and compressive sensing based novel image encryption scheme. J Inf Secur Appl 58:102711. https://doi.org/10.1016/j.jisa.2020.102711

    Article  Google Scholar 

  20. Kumar Patro KA, Acharya B (2019) An efficient colour image encryption scheme based on 1-D chaotic maps. J Inf Secur Appl 46:23–41. https://doi.org/10.1016/j.jisa.2019.02.006

    Article  Google Scholar 

  21. Liao X, Li K, Yin J (2017) Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform. Multimed Tools Appl 76(20):20739–20753. https://doi.org/10.1007/s11042-016-3971-4

    Article  Google Scholar 

  22. Liao X, Guo S, Yin J, Wang H, Li X, Sangaiah AK (2018) New cubic reference table based image steganography. Multimed Tools Appl 77(8):10033–10050. https://doi.org/10.1007/s11042-017-4946-9

    Article  Google Scholar 

  23. Lim WYB, Huang J, Xiong Z, Kang J, Niyato D, Hua X-S, Leung C, Miao C (2021) Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach. IEEE Trans Intell Transp Syst 22(8):5140–5154. https://doi.org/10.1109/TITS.2021.3056341

    Article  Google Scholar 

  24. Liu J, Zhang M, Tong X, Wang Z (2021) Image compression and encryption algorithm based on compressive sensing and nonlinear diffusion. Multimed Tools Appl 80(17):25433–25452. https://doi.org/10.1007/s11042-021-10884-2

    Article  Google Scholar 

  25. Madouri ZB, Hadj Said N, Ali Pacha A (2022) Image encryption algorithm based on digital filters controlled by 2D robust chaotic map. Optik 264:169382. https://doi.org/10.1016/j.ijleo.2022.169382

    Article  Google Scholar 

  26. Malik DS, Shah T (2020) Color multiple image encryption scheme based on 3D-chaotic maps. Math Comput Simul 178:646–666. https://doi.org/10.1016/j.matcom.2020.07.007

  27. Midoun MA, Wang X, Talhaoui MZ (2021) A sensitive dynamic mutual encryption system based on a new 1D chaotic map. Opt Lasers Eng 139:106485. https://doi.org/10.1016/j.optlaseng.2020.106485

    Article  Google Scholar 

  28. Musanna F, Kumar S (2020) A novel image encryption algorithm using chaotic compressive sensing and nonlinear exponential function. J Inf Secur Appl 54:102560. https://doi.org/10.1016/j.jisa.2020.102560

    Article  Google Scholar 

  29. Naim M, Ali Pacha A (2021) New chaotic satellite image encryption by using some or all the rounds of the AES algorithm. Inf Secur J: A Global Perspective 1–25. https://doi.org/10.1080/19393555.2021.1982082

  30. Naim M, Ali Pacha A, Serief C (2021) A novel satellite image encryption algorithm based on hyperchaotic systems and Josephus problem. Adv Space Res 67(7):2077–2103. https://doi.org/10.1016/j.asr.2021.01.018

    Article  Google Scholar 

  31. National Institute of Standards and Technology (2001) Advanced encryption standard (AES). Technical report #NIST FIPS 197. National Institute of Standards and Technology

  32. Pati YC, Rezaiifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. Proceedings of 27th Asilomar conference on signals, systems and computers (Pacific grove, CA, USA, 1993), 40–44

  33. Ponuma R, Amutha R (2018) Compressive sensing based image compression-encryption using Novel 1D-Chaotic map. Multimed Tools Appl 77(15):19209–19234. https://doi.org/10.1007/s11042-017-5378-2

    Article  Google Scholar 

  34. Salau, A.O., Oluwafemi, I., Faleye, K.F. and Jain, S. (2019). Audio compression using a modified discrete cosine transform with temporal auditory masking. 2019 international conference on signal processing and communication (ICSC) (NOIDA, India, mar. 2019), 135–142

  35. Shi Y, Hu Y, Wang B (2021) Image encryption scheme based on multiscale block compressed sensing and Markov model. Entropy 23(10):1297. https://doi.org/10.3390/e23101297

    Article  Google Scholar 

  36. Vanjari HB, Kolte MT (2022) Machine learning improvements to compressive sensing for speech enhancement in hearing aid applications. World J Eng 19(2):216–223. https://doi.org/10.1108/WJE-06-2021-0324

    Article  Google Scholar 

  37. Wang X, Yang J (2020) A novel image encryption scheme of dynamic S-boxes and random blocks based on spatiotemporal chaotic system. Optik 217:164884. https://doi.org/10.1016/j.ijleo.2020.164884

    Article  Google Scholar 

  38. Wang X, Liu L, Zhang Y (2015) A novel chaotic block image encryption algorithm based on dynamic random growth technique. Opt Lasers Eng 66:10–18. https://doi.org/10.1016/j.optlaseng.2014.08.005

    Article  Google Scholar 

  39. Wei D, Jiang M (2021) A fast image encryption algorithm based on parallel compressive sensing and DNA sequence. Optik 238:166748. https://doi.org/10.1016/j.ijleo.2021.166748

    Article  Google Scholar 

  40. Xie Y, Yu J, Guo S, Ding Q, Wang E (2019) Image encryption scheme with compressed sensing based on new three-dimensional chaotic system. Entropy 21(9):819. https://doi.org/10.3390/e21090819

    Article  MathSciNet  Google Scholar 

  41. Xie SR, Kotlarz P, Hennig RG, Nino JC (2020) Machine learning of octahedral tilting in oxide perovskites by symbolic classification with compressed sensing. Comput Mater Sci 180:109690. https://doi.org/10.1016/j.commatsci.2020.109690

    Article  Google Scholar 

  42. Xu Q, Sun K, Cao C, Zhu C (2019) A fast image encryption algorithm based on compressive sensing and hyperchaotic map. Opt Lasers Eng 121:203–214. https://doi.org/10.1016/j.optlaseng.2019.04.011

    Article  Google Scholar 

  43. Xu Q, Sun K, He S, Zhu C (2020) An effective image encryption algorithm based on compressive sensing and 2D-SLIM. Opt Lasers Eng 134:106178. https://doi.org/10.1016/j.optlaseng.2020.106178

    Article  Google Scholar 

  44. Xu J, Mou J, Liu J, Hao J (2022) The image compression–encryption algorithm based on the compression sensing and fractional-order chaotic system. Vis Comput 38(5):1509–1526. https://doi.org/10.1007/s00371-021-02085-7

    Article  Google Scholar 

  45. Yang Y-G, Guan B-W, Li J, Li D, Zhou Y-H, Shi W-M (2019) Image compression-encryption scheme based on fractional order hyper-chaotic systems combined with 2D compressed sensing and DNA encoding. Opt Laser Technol 119:105661. https://doi.org/10.1016/j.optlastec.2019.105661

    Article  Google Scholar 

  46. Yang L, Yang Q, Chen G (2020) Hidden attractors, singularly degenerate heteroclinic orbits, multistability and physical realization of a new 6D hyperchaotic system. Commun Nonlinear Sci Numer Simul 90:105362. https://doi.org/10.1016/j.cnsns.2020.105362

    Article  MathSciNet  MATH  Google Scholar 

  47. Zhang Y-Q, Wang X-Y (2015) A new image encryption algorithm based on non-adjacent coupled map lattices. Appl Soft Comput 26:10–20. https://doi.org/10.1016/j.asoc.2014.09.039

    Article  Google Scholar 

  48. Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Novel hybrid image compression–encryption algorithm based on compressive sensing. Optik 125(18):5075–5080. https://doi.org/10.1016/j.ijleo.2014.06.054

    Article  Google Scholar 

  49. Zhou N, Pan S, Cheng S, Zhou Z (2016) Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt Laser Technol 82:121–133. https://doi.org/10.1016/j.optlastec.2016.02.018

    Article  Google Scholar 

  50. Zhou K, Fan J, Fan H, Li M (2020) Secure image encryption scheme using double random-phase encoding and compressed sensing. Opt Laser Technol 121:105769. https://doi.org/10.1016/j.optlastec.2019.105769

    Article  Google Scholar 

  51. Zhu Z, Song Y, Zhang W, Yu H, Zhao Y (2020) A novel compressive sensing-based framework for image compression-encryption with S-box. Multimed Tools Appl 79(35–36):25497–25533. https://doi.org/10.1007/s11042-020-09193-x

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Coding and Security of Information, University of Sciences and Technology of Oran Mohamed Boudiaf, PoBox 1505, 31000, Oran, M’Naouer, Algeria

    A. Hadj Brahim, A. Ali Pacha & N. Hadj Said

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  1. A. Hadj Brahim
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  2. A. Ali Pacha
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Hadj Brahim, A., Ali Pacha, A. & Hadj Said, N. A new image compression-encryption scheme based on compressive sensing & classical AES algorithm. Multimed Tools Appl 82, 42087–42117 (2023). https://doi.org/10.1007/s11042-023-15171-w

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