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Secure Selective Image Encryption Technique for Real Time Applications

by Kiran, Parameshachari B.D.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 5
Year of Publication: 2022
Authors: Kiran, Parameshachari B.D.
10.5120/ijca2022922009

Kiran, Parameshachari B.D. . Secure Selective Image Encryption Technique for Real Time Applications. International Journal of Computer Applications. 184, 5 ( Mar 2022), 15-20. DOI=10.5120/ijca2022922009

@article{ 10.5120/ijca2022922009,
author = { Kiran, Parameshachari B.D. },
title = { Secure Selective Image Encryption Technique for Real Time Applications },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2022 },
volume = { 184 },
number = { 5 },
month = { Mar },
year = { 2022 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number5/32327-2022922009/ },
doi = { 10.5120/ijca2022922009 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:41.900283+05:30
%A Kiran
%A Parameshachari B.D.
%T Secure Selective Image Encryption Technique for Real Time Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 5
%P 15-20
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advent of medical imaging tools and telemedicine technology, patient information, medical imaging data is subject to strict data protection and confidentiality requirements. This raises the issue of sending medical image data within an open network due to the above issues, along with the risk of data / information leakage. Potential solutions in the past included the use of information hiding and image encryption techniques. However, these methods can cause problems when trying to restore the original image. In this work, developed an algorithm that protects medical images based on the pixels of interest. Image histogram peak detection for calculating peaks in medical images. Threshold value processed pixels of interest in medical images. The average of all peaks in the histogram indicates the threshold. These pixels are then encoded with interest values ​​using a Sudoku matrix. The proposed scheme will be evaluated using various statistical tests and these results will be compared to existing benchmarks. The results show that the proposed algorithm has better security performance compared to existing image encryption schemes.

References
  1. Satoh H, Niki N, Eguchi K, Ohmatsu H, Kusumoto M, Kaneko M, Moriyama N. Teleradiology network system on cloud using the web medical image conference system with a new information security solution. In: SPIE medical imaging, vol. 8674. Lake Buena Vista (Orlando Area): SPIE; 2013.
  2. Avudaiappan T, Balasubramanian R, Pandiyan SS, Saravanan M, Lakshmanaprabu SK, Shankar K. Medical image security using dual encryption with oppositional based optimization algorithm. J Med Syst. 2018;42(11):208.
  3. Wang C, Wang X, Xia Z, Zhang C. Ternary radial harmonic Fourier moments based robust stereo image zero-watermarking algorithm. Inf Sci. 2019;470:109–20.
  4. Xie, Dong. "Public Key Image Encryption Based on Compressed Sensing." IEEE Access 7 (2019): 131672-131680.
  5. Zhu, Shuqin, and Congxu Zhu. "Plaintext-Related Image Encryption Algorithm Based on Block Structure and Five-Dimensional Chaotic Map." IEEE Access 7 (2019): 147106-147118.
  6. Binjubeir, Mohammed, et al. "Comprehensive survey on big data privacy protection." IEEE Access 8 (2019): 20067-20079.
  7. Li, Ming, et al. "Cryptanalysis of a Novel Bit-Level Color Image Encryption Using Improved 1D Chaotic Map." IEEE Access 7 (2019): 145798-145806.
  8. Manjula and Mohan H S, "Probability based selective encryption scheme for fast encryption of medical images." (2019). SJBIT.
  9. Jang, Wonyoung, and Sun-Young Lee. "Partial image encryption using format-preserving encryption in image processing systems for Internet of things environment." International Journal of Distributed Sensor Networks 16.3 (2020): 1550147720914779.
  10. Sankaradass, Veeramalai, P. Murali, and M. Tholkapiyan. "Region of Interest (ROI) based image encryption with sine map and lorenz system." International Conference on ISMAC in Computational Vision and Bio-Engineering. Springer, Cham, 2018.
  11. Mousavi, SeyedMojtaba, AlirezaNaghsh, and S. A. R. Abu-Bakar. "A heuristic automatic and robust ROI detection method for medical image warermarking." Journal of digital imaging 28.4 (2015): 417-427.
  12. Zhou, Jian, Jinqing Li, and Xiaoqiang Di. "A Novel Lossless Medical Image Encryption Scheme Based on Game Theory With Optimized ROI Parameters and Hidden ROI Position." IEEE Access 8 (2020): 122210-122228.
  13. Kiran , Rashmi P, Dr. Supriya M C “Encryption of Color image to enhance security using Permutation and Diffusion Techniques”, International Journal of Advanced Science and Technology Vol. 28, No. 12, (2019), pp. 375-384.
  14. Zhicheng N, Yun-Qing S, Ansari N, Wei S. Reversible data hiding. IEEE Trans Circuits Syst Video Technol. 2006;16(3):354–62.
  15. Kumar CV, Natarajan V, Bhogadi D. High capacity reversible data hiding based on histogram shifting for medical images. In: 2013 international conference on communication and signal processing: 3–5 April 2013; Tamilnadu; 2013, p. 730–3.
  16. Yang Y, Zhang W, Yu N. Improving visual quality of reversible data hiding in medical image with texture area contrast enhancement. In: 2015 international conference on intelligent information hiding and multimedia signal processing (IIH-MSP): 23–25 Sept 2015; Adelaide; 2015. p. 81–4.
  17. Wu M, Zhao J, Chen B, Zhang Y, Yu Y, Cheng J. Reversible data hiding based on medical image systems by means of histogram strategy. In: 2018 3rd international conference on information systems engineering (ICISE): 4–6 May 2018; Shanghai; 2018. p. 6–9.
  18. Huang L-C, Tseng L-Y, Hwang M-S. A reversible data hiding method by histogram shifting in high quality medical images. J SystSoftw. 2013;86(3):716–27.
  19. Yue XD, Miao DQ, Zhang N, Cao LB, Wu Q (2012) Multiscale roughness measure for color image segmentation. InfSci 216(24):93–112
  20. Sastry SS, Mallika K, Rao BGS, Tiong HS, Lakshminarayana S (2012) Liquid crystal textural analysis based on histogram homogeneity and peak detection algorithm. LiqCryst 39(4): 415–418
  21. Boukharouba S, Rebordao JM, Wendel PL (1984) An amplitude segmentation method based on the distribution function of an image. Comput Vis Graph Image Process 29(1):47–59
  22. Elguebaly T, Bouguila N (2011) Bayesian learning of finite generalized gaussian mixture models on images. Sig Process 91(4):801–820.
  23. Azam M, Bouguila N (2015) Unsupervised keyword spotting using bounded generalized Gaussian mixture model with ICA. In: IEEE global conference on signal and information processing, pp 1150–1154.
  24. Nguyen TM, Wu QMJ, Zhang H (2014) Bounded generalized Gaussian mixture model. Pattern Recogn 47(9):3132–3142.
  25. P. Rashmi, M. C. Supriya, Qiaozhi Hua, "Enhanced Lorenz-Chaotic Encryption Method for Partial Medical Image Encryption and Data Hiding in Big Data Healthcare", Security and Communication Networks, vol. 2022, Article ID 9363377, 9 pages, 2022. https://doi.org/10.1155/2022/9363377.
  26. Rashmi . P, Supriya. M C and K. Kiran, "Image Encryption Algorithm based on 2D Hyper Chaotic Map and Trigonometric Functions", 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), 2021, pp. 1-4. doi: 10.1109/ICMNWC52512.2021.9688349.
  27. Rashmi P, Supriya M C, Building Enhanced Chaotic Map Encryption Method for Medical Information System, Journal of System and Management Sciences, Vol. 11 (2021) No. 1, pp. 176-192, DOI:10.33168/JSMS.2021.0111
  28. ArwaBenlashram, Maryam Al-Ghamdi, RawanAlTalhi and Pr. KaoutherLaabidi, “A novel approach of image encryption using pixel shuffling and 3D chaotic map”, Journal of Physics: Conference Series 1447 (2020) 012009. 10.1088/1742-6596/1447/1/012009.
  29. Jawad Ahmad and Fawad Ahmed. Efficiency analysis and security evaluation of image encryption schemes. computing, 23:25, 2012.
  30. XuncaiZhang ,Lingfei Wang , Guangzhao Cui , and Ying Niu, “Entropy-Based Block Scrambling Image Encryption Using DES Structure and Chaotic Systems” Hindawi International Journal of Optics, Volume 2019, Article ID 3594534, 13 pages https://doi.org/10.1155/2019/3594534.
  31. Yue Wu, Joseph P Noonan, and SosAgaian. Npcr and uaci randomness tests for image encryption. Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), pp 31-38, 2011.
  32. Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004.
  33. ZhouWang and Alan C Bovik. Modern image quality assessment. Synthesis Lectures on Image, Video, and Multimedia Processing, 2(1):1-156, 2006.
  34. Sajjad M, Muhammad K, Baik SW, Rho S, Jan Z, Yeo SS, Mehmood I (2017) Mobile-cloud assistedframework for selective encryption of medical images with steganography for resource-constrained devices. Multimed Tools Appl 76(3):3519–3536.
Index Terms

Computer Science
Information Sciences

Keywords

Pixels of Interest Medical Images Encryption Histogram Peak Detection

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