Abstract
A new image encryption algorithm is presented based on a novel chaotic map and compressive sensing with excellent performance. At first, the sparse coefficient matrix is acquired by discrete wavelet transform (DWT) of the original image. Secondly, the SHA-512 hash value of the original image are regarded as the initial values of two novel 1D chaotic maps to generate two chaotic sequences. Furthermore, the measurement matrix is generated by one of the two chaotic maps. Next, the measurement result is scrambled based on bit-plane operations by another chaotic sequence. At last, the diffusion and rotation operations are carried out on the shuffled matrix to improve the security index of the proposed algorithm. It is indicated through the analyses of simulation experiment that the presented encryption algorithm is effective to resist statistical attacks, plaintext attacks and brute-force attacks and has good compression effect and robustness.
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References
Baraniuk R (2007) Compressive sensing (lecture notes). IEEE Signal Process Mag 24(4):118–121
Candes EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52:489–509
Cao C, Sun K, Liu W. (2017) A novel bit-level image encryption algorithm based on 2D-LICM hyperchaotic map. Signal processing 143(2.):122-133.
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
Chai X, Fu X, Gan Z et al (2018) An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata. Neural Comput Applic 32:4961–4988
Chen J, Zhang Y, Qi L, Fu C , Xu L (2018) Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression Optics & Laser Technology, S0030399217305534.
Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theor 52:1289–1306
Han J, Claudio M (1995) The influence of the sigmoid function parameters on the speed of back propagation learning. International Workshop on Artificial Neural Networks: from Natural to Artificial Neural Computation Springer-Verlag:195–201.
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 R 44:116–127
Huang Y et al (2019) Analysis and FPGA realization of a novel 5D Hyperchaotic four-wing Memristive system, active control synchronization, and secure communication application. Complexity 12:1–18
Idrees B, Zafar S, Rashid T, Gao W (2020) Image encryption algorithm using S-box and dynamic Hénon bit level permutation. Multimed Tools Appl 79(9–10):6135–6162
Jain A, Rajpal N (2016) A robust image encrytion algorithm resistant to attacks using DNA and chaotic logistic maps. Mutitimed Tools Appl 75:5455–5472
Kumar RR, Kumar MB (2014) A new chaotic image encryption using parametric switching based permutation and diffusion. Ictact J Image Video Process 4(5):795–804(10)
Li T, Yang M, Wu J, Jing X (2017) A Novel Image Encryption Algorithm Based on a Fractional-Order Hyper chaotic System and DNA Computing. Complexity:1–13
Liu XY, Cao YP, Lu P, Lu X, Li Y (2013) Optical image encryption technique based on compressed sensing and Arnold transformation. Optik 124(24):6590–6593
Liu J, Yang D, Zhou H, Chen S (2018) A digital image encryption algorithm based on bit-planes and an improved logistic map. Multimed Tools Appl 77(8):10217–10233
Luo Y, Qin J, Xiang X, Tan Y, Liu Q, Xiang L (2020) Coverless real-time image information hiding based on image block matching and dense convolutional network. J Real-Time Image Proc 17(1):125–135
Lv-Chen C, Yu-Ling L, Sen-Hui Q, Jun-Xiu L (2015) A perturbation method to the tent map based on Lyapunov exponent and its application. Chin Phys B 24(10):78–85
Machkour, M. , Saaidi, A. , & Benmaati, M. L. . (2015). A novel image encryption algorithm based on the two-dimensional logistic map and the latin square image cipher. 3D Research.
Mallat SG, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 41(12): 3397–3415.
Mirzaei O, Yaghoobi M, Irani H (2012) A new image encryption method: parallel sub-image encryption with hyperchaos. Nonlinear Dyn 67(1):557–566
Norouzi B, Seyedzadeh SM, Mirzakuchaki S, Mosavi MR (2015) A novel image encryption based on row-column, masking and main diffusion processes with hyper chaos. Multimed Tools Appl 74(3):781–811
Pak C, Huang L A new color image encryption using combination of the 1D chaotic map. Signal Processing 138:129–137.
Pak C, An K, Jang P (2017) A novel bit-level color image encryption using improved 1D chaotic map. Multimedia Tools Appl 78(9):12027–12042
Pati YC, Rezaiifar R, And P. S. Krishnaprasad orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In Proc. of the 27th annual Asilomar conference on signals, Systems and Computers
Ponuma R, Amutha R (2018) Compressive sensing based image compression-encryption using novel 1D-chaotic map. Multimed Tools Appl 77(15):19209–19234
Sangeetha Y, Meenakshi S, Sundaram CS (2014) A simple, sensitive and secure image encryption algorithm based on hyper-chaotic system with only one round diffusion process. Multimed Tools Appl 71(3):1469–1497
Shaila SG, Vadivel A (2012) Block encoding of color histogram for content based image retrieval applications. Procedia Technology 6:526–533
Shaila SG, Vadivel A (2016) Indexing and encoding based image feature representation with bin overlapped similarity measure for CBIR applications. Journal of Visual Communication & Image Representation 36(4):40–55
Shao W, Cheng M, Luo C et al (2019) An image encryption scheme based on hybrid electro-optic chaotic sources and compressive sensing. IEEE Access 7:1–1
Shao W, Cheng M , Luo C, Deng L , Liu D (2019) An image encryption scheme based on hybrid electro-optic chaotic sources and compressive sensing. IEEE access 1-1.
Wang X, Liu C (2017) A novel and effective image encryption algorithm based on chaos and DNA encoding. Multimed Tools Appl 76(5):6229–6245
Ye G, Pan C, Huang X, Zhao Z, and He J (2018) A chaotic image encryption algorithm based on information entropy. Int. J. Bifurcation Chaos 28 (1): Art. no. 1850010.
Ye G, Pan C, Huang X, Zhao Z, and He J (2018) A chaotic image encryption algorithm based on information entropy. Int. J. Bifurcation Chaos 28 (1):Art. no. 1850010.
Yin X, Zhang M, Wang L, Liu Y (2020) Interface debonding performance of precast segmental nano-materials based concrete (PSNBC) beams. Mater Express 10(8):1317–1327
Yu L, Barbot JP, Zheng G et al (2010) Compressive sensing with chaotic sequence[J]. IEEE Signal Processing Letters 17(8):731–734
Zhang Y, Xiao D (2013) Cryptanalysis of S-box-only chaotic image ciphers against chosen plaintext attack. Nonlinear Dynamics 72(4):751–756
Zhang YS, Xiao D, Shu YL, Li J (2011) A novel image encryption scheme based on a linear hyperbolic chaotic sys-tem of partial differential equations. Signal process-image Commun. 28(3), 292–300 (2013)
Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386
Zhang Y, Xiao D, Wen W, Li M (2014) Breaking an image encryption algorithm based on hyper-chaotic system with only one round diffusion process. Nonlinear Dyn 76(3):1645–1650
Zhang Z et al (2020) Dynamic analysis, circuit design, and synchronization of a novel 6D Memristive four-wing Hyperchaotic system with multiple coexisting attractors. Complexity 12:1–17
Zhang J et al (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 99:1–1
Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Novel hybrid image compression–encryption algorithm based on compressive sensing. Optik-International Journal for Light and Electron Optics 125(18):5075–5508
Zhou Y, Bao L, Chen CLP (2014) A new 1D chaotic system for image encryption. Signal Process 97:172–182
Zhou NR, Pan SM, Cheng S, Zhou ZH (2016) Image compression encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt Laser Technol 82:121–133
Zhu K, Cheng J (2020) Color image encryption via compressive sensing and chaotic systems. MATEC Web of Conferences 309:03017
Zhu H, Zhao C, Zhang X (2013) A novel image encryption–compression scheme using hyper-chaos and Chinese remainder theorem. Signal Process-Image 28(6):670–680
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant no. 62072159, U1804164) and PhD Scientific Research Foundation of Henan Normal University (Grant no. qd18027).
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Dou, Y., Li, M. An image encryption algorithm based on a novel 1D chaotic map and compressive sensing. Multimed Tools Appl 80, 24437–24454 (2021). https://doi.org/10.1007/s11042-021-10850-y
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DOI: https://doi.org/10.1007/s11042-021-10850-y