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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Hore A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. 2010 20th international conference on pattern recognition (Istanbul, Turkey, Aug. 2010), 2366–2369
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
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
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
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
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
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
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
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
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
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
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
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
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
National Institute of Standards and Technology (2001) Advanced encryption standard (AES). Technical report #NIST FIPS 197. National Institute of Standards and Technology
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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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DOI: https://doi.org/10.1007/s11042-023-15171-w