A Secure Chaotic Framework for Medical Image Encryption using a 3D Logistic Map

E A Adedokun, Bello J Akan, H B Salau, I J Umoh, Richard I Nwosu, Yunusa Ibrahim

Abstract

Information security and privacy are of utmost importance in transmitting, storing and preserving medical images. In this paper, a secure chaotic framework for medical image encryption is presented, in order to address existing defect in chaos-based image encryption algorithm. The proposed algorithm does the following: a 2D logistic adjusted sine map (2D-LASM) is utilized to generate random number with pixel format, the generated number are then inserted on a plain-image surrounding, which is then divided into non overlapping sub blocks. The scrambling sequence were generated using a 3D logistic map, before image pixel blocks scrambling and diffusion were performed on each sub-block of pixels  using the generated scrambling sequence. To verify the efficiency of the proposed algorithm, a number of simulations were carried-out. Based on the results obtained, the algorithm recorded an average score of 7.9998 for information entropy while for number of pixel change rate (NPCR) and unified average changing intensity (UACI) 99.6261% and 33.4830% respectively on an average scale. The proposed algorithm showed high robustness, sensitivity and resistance to all forms of differential attack.

Keywords

Block-level scrambling; Image blocking; Image encryption; Medical image.

Article Metrics

Abstract view : 342 times
PDF - 346 times

Full Text:

PDF

References

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

G. Alvarez, S. Li and L. Hernandez, Analysis of security problems in a medical image encryption system, Computers in Biology and Medicine, 37, 2007, 424–427.

C. Fu, G.-y. Zhang, O. Bian, W.-m. Lei, and H.-f. Ma, A novel medical image protection scheme using a 3-dimensional chaotic system, PloS One, 9, 2014, 1–25.

G. Ye, K. Jiao, C. Pan and X. Huang, An effective framework for chaotic image encryption based on 3D logistic map, Security and Communication Networks, 2018, 1–11.

P. T. Akkasaligar and S. Biradar, Secure medical image encryption based on intensity level using chao's theory and DNA cryptography, 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016, 1–6.

A. S. Nasim, Chaos based cryptogrphy and image encryption, M.Sc. Dissertation, Electrical and Computer Engineering, University of Applied Sciences, Luebeck, Germany, 2015.

M. B. Hossain, M. T. Rahman, A. S. Rahman and S. Islam, A new approach of image encryption using 3D chaotic map to enhance security of multimedia component, 2014 International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka, Bangladesh, 2014, 1–6.

M. Machkour, A. Saaidi and M. L. Benmaati, A novel image encryption algorithm based on the two-dimensional logistic map and the latin square image cipher, 3D Research, 6(36), 2015, 1–18.

N. Mekki, M. Hamdi, T. Aguili and T.-h. Kim, A real-time chaotic encryption for multimedia data and application to secure surveillance framework for IoT system, 2018 International Conference on Advanced Communication Technologies and Networking (CommNet), Marrakech, Morocco, 2018, 1–10.

H. Oğraş and M. Türk, A secure chaos-based image cryptosystem with an improved sine key generator, American Journal of Signal Processing, 6(3), 2016, 67–76.

W. Zhang, H. Yu, Y.-l. Zhao and Z.-l. Zhu, Image encryption based on three-dimensional bit matrix permutation, Signal Processing,118, 2016, 36–50.

L. Xu, Z. Li, J. Li and W. Hua, A novel bit-level image encryption algorithm based on chaotic maps, Optics and Lasers in Engineering, 78, 2016, 17–25.

A.-V. Diaconu, V. Ionescu, G. Iana and J. M. Lopez-Guede, A new bit-level permutation image encryption algorithm, 2016 International Conference on Communications (COMM), Bucharest, Romania, 2016, 411–416.

N. K. Pareek, V. Patidar and K. K. Sud, Image encryption using chaotic logistic map, Image and Vision Computing, 24, 2006, 926–934.

J.-Q. Cao, H.-R. Xiao and Z.-L. Lan, Chaos encryption algorithm based on dual scrambling of pixel position and value, Computer Engineering and Applications, 46, 2010, 192–195.

W. Zhen, H. Xia, L. Yu-Xia and S. Xiao-Na, A new image encryption algorithm based on the fractional-order hyperchaotic Lorenz system, Chinese Physics B, 22(1), 2013, 010504.

J.-x. Chen, Z.-l. Zhu, C. Fu and H. Yu, An improved permutation-diffusion type image cipher with a chaotic orbit perturbing mechanism, Optics Express, 21, 2013, 27873–27890.

Y. Zhang and D. Xiao, An image encryption scheme based on rotation matrix bit-level permutation and block diffusion, Communications in Nonlinear Science and Numerical Simulation, 19, 2014, 74–82.

Y. Li, C. Wang and H. Chen, A hyper-chaos-based image encryption algorithm using pixel-level permutation and bit-level permutation, Optics and Lasers in Engineering, 90, 2017, 238–246.

K. Muhammad, R. Hamza, J. Ahmad, J. Lloret, H. Wang and S. W. Baik, Secure surveillance framework for IoT systems using probabilistic image encryption, IEEE Transactions on Industrial Informatics, 14, 2018, 3679–3689.

Z. Hua and Y. Zhou, Image encryption using 2D Logistic-adjusted-sine map, Information Sciences, 339, 2016, 237–253.

H. Li, Y. Wang and Z. Zuo, Chaos-based image encryption algorithm with orbit perturbation and dynamic state variable selection mechanisms, Optics and Lasers in Engineering, 115, 2019, 197–207.

C. Fu, Y.-F. Shan, M.-Y. He, Z.-Y. Yu and H.-L. Wu, A new medical image encryption algorithm using multiple 1-D chaotic maps, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, 2055–2060.

X. Chen and C.-J. Hu, Adaptive medical image encryption algorithm based on multiple chaotic mapping, Saudi Journal of Biological Sciences, 24(8), 2017, 1821–1827.

Y. Chen, C. Tang and R. Ye, Cryptanalysis and improvement of medical image encryption using high-speed scrambling and pixel adaptive diffusion, Signal Processing, 167, 2019, 107286.

M. Levoy, The Stanford volume data archive, 2001. https://graphics.stanford.edu/data/voldata, 2009. (accessed 15.07.2019).

SPIE-AAPM Lung CTchallenge. https://scidm.nchc.org.tw/en/dataset/spieaapmlungctchallenge/resource/1a37c6c8-10aa-4a9f-8db3-6e632bf788d7 (accessed 05.09.2019).

Refbacks

  • There are currently no refbacks.