A Survey of Image Encryption Algorithms for Biomedical Images

Mohan Manju*, Rajesh Kumar Pathak**
* Department of Computer Science, Shri Rawatpura Sarkar University, Raipur, Chhattisgarh, India.
** OPJS University, Rajasthan, India.
Periodicity:July - December'2022
DOI : https://doi.org/10.26634/jpr.9.2.19091

Abstract

Biomedical imaging is a powerful tool for visualizing the body's internal organs and illnesses. Image protection is difficult in the field of biomedical imaging. Many studies have been conducted in the medical field to protect medical images. Encryption is the perfect solution for image privacy without data loss. Due to data size, redundancy, and performance limitations, traditional encryption methods cannot be directly applied to e-health medical data, especially when patient data is transferred through open channels. Thus, patients may experience a loss of confidentiality of the data content because images differ from text due to two different factors, such as loss of data and loss of privacy. Researchers have determined such protection risks and suggested several image encryption strategies for undesirable security difficulties. However, the study found that the currently offered approaches face application-specific security issues. Therefore, there is a growing demand to protect sensitive information in medical images. This paper presents a color and grayscale medical image encoding algorithm to preserve medical images. The compression and encoding performance of the proposed algorithm are analyzed and evaluated based on Mean Squared Error (MSE), Peak Signalto- Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), statistics, diversity, and entropy analysis. Matrix Laboratory (MATLAB) results show that the proposed algorithm provides high-quality reconstructed images with a good level of security during transmission.

Keywords

Medical Image Processing, Digital Image Encryption, Secure Communication, Encryption Algorithm.

How to Cite this Article?

Manju, M., and Pathak, R. K. (2022). A Survey of Image Encryption Algorithms for Biomedical Images. i-manager’s Journal on Pattern Recognition, 9(2), 37-45. https://doi.org/10.26634/jpr.9.2.19091

References

[1]. Akkasaligar, P. T., & Biradar, S. (2020). Selective medical image encryption using DNA cryptography. Information Security Journal: A Global Perspective, 29(2), 91-101. https://doi.org/10.1080/19393555.2020.1718248
[2]. Alsaffar, D. M., Almutiri, A. S., Alqahtani, B., Alamri, R. M., Alqahtani, H. F., Alqahtani, N. N., & Ali, A. A. (2020, March). Image encryption based on AES and RSA algorithms. In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCAIS48893.2020.9096809
[3]. Cao, C., Sun, K., & Liu, W. (2018). A novel bit-level image encr yption algorithm based on 2D-LICM hyperchaotic map. Signal Processing, 143, 122-133. https://doi.org/10.1016/j.sigpro.2017.08.020
[4]. Chai, X., Fu, X., Gan, Z., Lu, Y., & Chen, Y. (2019). A color image cryptosystem based on dynamic DNA encryption and chaos. Signal Processing, 155, 44-62. https://doi.org/10.1016/j.sigpro.2018.09.029
[5]. Chepuri, S. (2017). An RGB image encryption using RSA algorithm. International Journal of Current Trends in Engineering & Research (IJCTER), 3(3), 2455-1392.
[6]. Devi, A., Sharma, A., & Rangra, A. (2015). A review on DES, AES and blowfish for image encryption & decryption. International Journal of Computer Science and Information Technologies, 6(3), 3034-3036.
[7]. Fu, X. Q., Liu, B. C., Xie, Y. Y., Li, W., & Liu, Y. (2018). Image encr yption-then-transmission using DNA encryption algorithm and the double chaos. IEEE Photonics Journal, 10(3), 1-15. https://doi.org/10.1109/JPHOT.2018.2827165
[8]. Gamido, H. V., Sison, A. M., & Medina, R. P. (2018). Modified AES for text and image encryption. Indonesian Journal of Electrical Engineering and Computer Science, 11(3), 942-948. https://doi.org/10.11591/ijeecs.v11.i3
[9]. Gaur, P., & Kaushik, A. (2021). AES image encryption (Advanced encryption standard). International Journal for Research in Applied Science and Engineering Technology, 9(12), 1357-1363. https://doi.org/10.22214/ijraset.2021.39542
[10]. Narasingapuram, P. B., & Ponnavaikko, M. (2021). A novel attack detection and encryption framework for distributed cloud computing. Indian Journal of Computer Science and Engineering, 12(1), 210-216. https://doi.org/10.21817/indjcse/2021/ v12i1/211201249
[11]. Nematzadeh, H., Enayatifar, R., Motameni, H., Guimarães, F. G., & Coelho, V. N. (2018). Medical image encryption using a hybrid model of modified genetic algorithm and coupled map lattices. Optics and Lasers in Engineering, 110, 24-32. https://doi.org/10.1016/j.optlaseng.2018.05.009
[12]. Ning, J., Xu, J., Liang, K., Zhang, F., & Chang, E. C. (2019). Passive attacks against searchable encryption. IEEE Transactions on Information Forensics and Security, 14(3), 789-802. https://doi.org/10.1109/TIFS.2018.2866321
[13]. Parida, P., Pradhan, C., Gao, X. Z., Roy, D. S., & Barik, R. K. (2021). Image encryption and authentication with elliptic curve cryptography and multidimensional chaotic maps. IEEE Access, 9, 76191-76204. https://doi.org/10.1109/ACCESS.2021.3072075
[14]. Zhang, Q., Guo, L., & Wei, X. (2010). Image encryption using DNA addition combining with chaotic maps. Mathematical and Computer Modelling, 52(11- 12), 2028-2035. https://doi.org/10.1016/j.mcm.2010.06.005
[15]. Zhang, Y. (2018). Test and verification of AES used for image encryption. 3D Research, 9(1), 1-27. https://doi.org/10.1007/s13319-017-0154-7
[16]. Zhang, Y. Q., Hao, J. L., & Wang, X. Y. (2020). An efficient image encryption scheme based on S-boxes and fractional-order differential logistic map. IEEE Access, 8, 54175-54188. https://doi.org/10.1109/ACCESS.2020.2979827
[17]. Zhou, L., Xiao, Y., & Chen, W. (2019). Machinelearning attacks on inter ference-based optical encryption: experimental demonstration. Optics Express, 27(18), 26143-26154. https://doi.org/10.1364/OE.27.026143
[18]. Zhu, K., Lin, Z., & Ding, Y. (2019). A new RSA image encr yption algorithm based on singular value decomposition. International Journal of Pattern Recognition and Artificial Intelligence, 33(1), 1954002. https://doi.org/10.1142/S0218001419540028
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.