Abstract
The main objective of this paper is to provide an efficient image encryption for each and every single person in order to secure their own records while saving them in social networks. We have formulated the delayed fuzzy cellular neural networks (FCNNs) with suitable keys that are the values of the parameters of FCNNs and obtain the irregular dynamical signal (solution) which encrypts the images. We have utilized entirely 42 parameters as a key sensitivity in the order of 10−15 among them three elements of initial condition parameters are sensitive to the order of 10−14. Lastly, comparison results are provided with the existing literature. The measurements show that the proposed algorithm is a novel overall solution for image encryption.
Funding source: Ministry of Higher Education Malaysia
Award Identifier / Grant number: FRGS/1/2020/STG06/SYUC/02/1
Funding source: UCSI University
Award Identifier / Grant number: REIG-FBM-2020/033
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: This research work was supported by the UCSI University Research Excellence & Innovation Grant (REIG), Project Code REIG-FBM-2020/033. S. H. Ong is also supported by Ministry of Higher Education grant FRGS/1/2020/STG06/SYUC/02/1.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
[1] L. Yu, Z. Wang, and W. Wang, “The application of hybrid encryption algorithm in software security,” in 4th IEEE International Conference on Computational Intelligence and Communication Networks, 2012, pp. 762–765.10.1109/CICN.2012.195Search in Google Scholar
[2] R. Kaur and E. K. Singh, “Image encryption techniques: a selected review,” J. Comput. Eng. (IOSR-JCE), vol. 9, pp. 80–83, 2013. https://doi.org/10.9790/0661-0968083.Search in Google Scholar
[3] M. Kalpana, K. Ratnavelu, P. Balasubramaniam, and M. Z. M. Kamali, “Synchronization of chaotic-type delayed neural networks and its application,” Nonlinear Dynam., vol. 93, pp. 543–555, 2018. https://doi.org/10.1007/s11071-018-4208-z.Search in Google Scholar
[4] X. Y. Wang and Z. M. Li, “A color image encryption algorithm based on Hopfield chaotic neural network,” Opt Laser. Eng., vol. 115, pp. 107–118, 2019. https://doi.org/10.1016/j.optlaseng.2018.11.010.Search in Google Scholar
[5] T. Dong and T. Huang, “Neural cryptography based on complex-valued neural network,” IEEE Transact. Neural Networks Learn. Syst., vol. 31, pp. 4999–5004, 2020. https://doi.org/10.1109/TNNLS.2019.2955165.Search in Google Scholar PubMed
[6] A. Y. Niyat, M. H. Moattar, and M. N. Torshiz, “Color image encryption based on hybrid hyper-chaotic system and cellular automata,” Opt Laser. Eng., vol. 90, pp. 225–237, 2017. https://doi.org/10.1016/j.optlaseng.2016.10.019.Search in Google Scholar
[7] K. Ratnavelu, M. Kalpana, P. Balasubramaniam, K. Wong, and P. Raveendran, “Image encryption method based on chaotic fuzzy cellular neural networks,” Signal Process., vol. 140, pp. 87–96, 2017. https://doi.org/10.1016/j.sigpro.2017.05.002.Search in Google Scholar
[8] X. Wang, Y. Zhao, H. Zhang, and K. Guo, “A novel color image encryption scheme using alternate chaotic mapping structure,” Opt Laser. Eng., vol. 82, pp. 79–86, 2016. https://doi.org/10.1016/j.optlaseng.2015.12.006.Search in Google Scholar
[9] H. I. Hsiao and J. Lee, “Color image encryption using chaotic nonlinear adaptive filter,” Signal Process., vol. 117, pp. 281–309, 2015. https://doi.org/10.1016/j.sigpro.2015.06.007.Search in Google Scholar
[10] H. Liu and A. Kadir, “Asymmetric color image encryption scheme using 2D discrete-time map,” Signal Process., vol. 113, pp. 104–112, 2015. https://doi.org/10.1016/j.sigpro.2015.01.016.Search in Google Scholar
[11] X. Wang and H. Zhang, “A color image encryption with heterogeneous bit-permutation and correlated chaos,” Opt Commun., vol. 342, pp. 51–60, 2015. https://doi.org/10.1016/j.optcom.2014.12.043.Search in Google Scholar
[12] X. Wu, H. Kan, and J. Kurths, “A new color image encryption scheme based on DNA sequences and multiple improved 1D chaotic maps,” Appl. Soft Comput., vol. 37, pp. 24–39, 2015. https://doi.org/10.1016/j.asoc.2015.08.008.Search in Google Scholar
[13] C. Dong, “Color image encryption using one-time keys and coupled chaotic systems,” Signal Process. Image Commun., vol. 29, pp. 628–640, 2014. https://doi.org/10.1016/j.image.2013.09.006.Search in Google Scholar
[14] H. Liu and X. Wang, “Triple-image encryption scheme based on one-time key stream generated by chaos and plain images,” J. Syst. Software, vol. 86, pp. 826–834, 2013. https://doi.org/10.1016/j.jss.2012.11.026.Search in Google Scholar
[15] N. Bigdeli, Y. Farid, and K. Afshar, “A novel image encryption/decryption scheme based on chaotic neural networks,” Eng. Appl. Artif. Intell., vol. 25, pp. 753–765, 2012. https://doi.org/10.1016/j.engappai.2012.01.007.Search in Google Scholar
[16] X. Wei, L. Guo, Q. Zhang, J. Zhang, and S. Lian, “A novel color image encryption algorithm based on DNA sequence operation and hyper-chaotic system,” J. Syst. Software, vol. 85, pp. 290–299, 2012. https://doi.org/10.1016/j.jss.2011.08.017.Search in Google Scholar
[17] H. Liu and X. Wang, “Color image encryption using spatial bit-level permutation and high-dimension chaotic system,” Opt Commun., vol. 284, pp. 3895–3903, 2011. https://doi.org/10.1016/j.optcom.2011.04.001.Search in Google Scholar
[18] T. Yang, L. B. Yang, C. W. Wu, and L. O. Chua, “Fuzzy cellular neural networks: theory,” in Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications, 1996, pp. 181–186.10.1109/CNNA.1996.566545Search in Google Scholar
[19] T. Yang, L. B. Yang, C. W. Wu, and L. O. Chua, “Fuzzy cellular neural networks: applications,” in Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications, 1996, pp. 225–230.10.1109/CNNA.1996.566560Search in Google Scholar
[20] P. Balasubramaniam, M. Kalpana, and R. Rakkiyappan, “Existence and global asymptotic stability of fuzzy cellular neural networks with time delay in the leakage term and unbounded distributed delays,” Circ. Syst. Signal Process., vol. 30, pp. 1595–1616, 2011. https://doi.org/10.1007/s00034-011-9288-7.Search in Google Scholar
[21] L. Shanmugam and Y. H. Joo, “Investigation on stability of delayed TS fuzzy interconnected systems via decentralized memory-based sampled-data control and validation through interconnected power systems with DFIG-based wind turbines,” Inf. Sci., vol. 580, pp. 934–952, 2021. https://doi.org/10.1016/j.ins.2021.10.020.Search in Google Scholar
[22] P. Mani, R. Rajan, L. Shanmugam, and Y. H. Joo, “Adaptive control for fractional order induced chaotic fuzzy cellular neural networks and its application to image encryption,” Inf. Sci., vol. 491, pp. 74–89, 2019. https://doi.org/10.1016/j.ins.2019.04.007.Search in Google Scholar
[23] K. Ratnavelu, M. Kalpana, P. Balasubramaniam, K. Wong, and P. Raveendran, “Image encryption method based on chaotic fuzzy cellular neural networks,” Signal Process., vol. 140, pp. 87–96, 2017. https://doi.org/10.1016/j.sigpro.2017.05.002.Search in Google Scholar
[24] J. Shi, S. Chen, T. Chen, et al.., “Image encryption with quantum cellular neural network,” Quant. Inf. Process., vol. 21, pp. 1–29, 2022. https://doi.org/10.1007/s11128-022-03555-0.Search in Google Scholar
[25] M. J. Al-Muhammed and A. Al-Daraiseh, “Encryption technique based on fuzzy neural network hiding module and effective distortion method,” Neural Comput. Appl., vol. 34, pp. 9613–9633, 2022. https://doi.org/10.1007/s00521-022-06950-x.Search in Google Scholar
[26] S. Kanwal, S. Inam, O. Cheikhrouhou, K. Mahnoor, A. Zaguia, and H. Hamam, “Analytic study of a novel color image encryption method based on the chaos system and color codes,” Complexity, vol. 2021, pp. 1–19, 2021. https://doi.org/10.1155/2021/5499538.Search in Google Scholar
[27] The USC-SIPI Image Database. Available at: http://sipi.usc.edu/database/database.php.Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Original Research Articles
- Image-based 3D reconstruction precision using a camera mounted on a robot arm
- Switched-line network with digital phase shifter
- M-lump waves and their interactions with multi-soliton solutions for the (3 + 1)-dimensional Jimbo–Miwa equation
- Optimal control for a class of fractional order neutral evolution equations
- Perceptual evaluation for Zhangpu paper-cut patterns by using improved GWO-BP neural network
- Two new iterative schemes to approximate the fixed points for mappings
- Ulam’s type stability of impulsive delay integrodifferential equations in Banach spaces
- Generalization method of generating the continuous nested distributions
- Wellposedness of impulsive functional abstract second-order differential equations with state-dependent delay
- Numerical study of heat and mass transfer on the pulsatile flow of blood under atherosclerotic condition
- Dynamic propagation behaviors of pure mode I crack under stress wave loading by caustics
- Numerical simulation of buoyancy-induced heat transfer and entropy generation in 3D C-shaped cavity filled with CNT–Al2O3/water hybrid nanofluid
- On coupled system of nonlinear Ψ-Hilfer hybrid fractional differential equations
- Hellinger–Reissner variational principle for a class of specified stress problems
- Viscous dissipation effect on steady natural convection Couette flow with convective boundary condition
- Fredholm determinants and Z n -mKdV/Z n -sinh-Gordon hierarchies
- New soliton waves and modulation instability analysis for a metamaterials model via the integration schemes
- A modified high-order symmetrical WENO scheme for hyperbolic conservation laws
- Cryptanalysis of various images based on neural networks with leakage and time varying delays
- Spectral collocation method approach to thermal stability of MHD reactive squeezed fluid flow through a channel
- Higher order Traub–Steffensen type methods and their convergence analysis in Banach spaces
Articles in the same Issue
- Frontmatter
- Original Research Articles
- Image-based 3D reconstruction precision using a camera mounted on a robot arm
- Switched-line network with digital phase shifter
- M-lump waves and their interactions with multi-soliton solutions for the (3 + 1)-dimensional Jimbo–Miwa equation
- Optimal control for a class of fractional order neutral evolution equations
- Perceptual evaluation for Zhangpu paper-cut patterns by using improved GWO-BP neural network
- Two new iterative schemes to approximate the fixed points for mappings
- Ulam’s type stability of impulsive delay integrodifferential equations in Banach spaces
- Generalization method of generating the continuous nested distributions
- Wellposedness of impulsive functional abstract second-order differential equations with state-dependent delay
- Numerical study of heat and mass transfer on the pulsatile flow of blood under atherosclerotic condition
- Dynamic propagation behaviors of pure mode I crack under stress wave loading by caustics
- Numerical simulation of buoyancy-induced heat transfer and entropy generation in 3D C-shaped cavity filled with CNT–Al2O3/water hybrid nanofluid
- On coupled system of nonlinear Ψ-Hilfer hybrid fractional differential equations
- Hellinger–Reissner variational principle for a class of specified stress problems
- Viscous dissipation effect on steady natural convection Couette flow with convective boundary condition
- Fredholm determinants and Z n -mKdV/Z n -sinh-Gordon hierarchies
- New soliton waves and modulation instability analysis for a metamaterials model via the integration schemes
- A modified high-order symmetrical WENO scheme for hyperbolic conservation laws
- Cryptanalysis of various images based on neural networks with leakage and time varying delays
- Spectral collocation method approach to thermal stability of MHD reactive squeezed fluid flow through a channel
- Higher order Traub–Steffensen type methods and their convergence analysis in Banach spaces