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Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

Mesfer Al Duhayyim1,*, Fatma S. Alrayes2, Saud S. Alotaibi3, Sana Alazwari4, Nasser Allheeib5, Ayman Yafoz6, Raed Alsini6, Amira Sayed A. Aziz7

1 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
4 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif, 21944, Saudi Arabia
5 Department of Information Systems-College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
6 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
7 Department of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Mesfer Al Duhayyim. Email: email

Computer Systems Science and Engineering 2023, 46(3), 3209-3223. https://doi.org/10.32604/csse.2023.034185

Abstract

The Internet of Things (IoT) offers a new era of connectivity, which goes beyond laptops and smart connected devices for connected vehicles, smart homes, smart cities, and connected healthcare. The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users. With the increasing use of multimedia in communications, the content security of remote-sensing images attracted much attention in academia and industry. Image encryption is important for securing remote sensing images in the IoT environment. Recently, researchers have introduced plenty of algorithms for encrypting images. This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption (ISCACE-RSI) technique in IoT Environment. The proposed model follows a three-stage process, namely pre-processing, encryption, and optimal key generation. The remote sensing images were preprocessed at the initial stage to enhance the image quality. Next, the ISCACE-RSI technique exploits the double-layer remote sensing image encryption (DLRSIE) algorithm for encrypting the images. The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid (DNA) Strand Displacement (DNASD) approach. The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images. Then, the study presents three DNASD-related encryption rules based on the variety of DNASD, and those rules are applied for encrypting the images at the DNA sequence level. For an optimal key generation of the DLRSIE technique, the ISCA is applied with an objective function of the maximization of peak signal to noise ratio (PSNR). To examine the performance of the ISCACE-RSI model, a detailed set of simulations were conducted. The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.

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Cite This Article

APA Style
Duhayyim, M.A., Alrayes, F.S., Alotaibi, S.S., Alazwari, S., Allheeib, N. et al. (2023). Remote sensing image encryption using optimal key generation-based chaotic encryption. Computer Systems Science and Engineering, 46(3), 3209-3223. https://doi.org/10.32604/csse.2023.034185
Vancouver Style
Duhayyim MA, Alrayes FS, Alotaibi SS, Alazwari S, Allheeib N, Yafoz A, et al. Remote sensing image encryption using optimal key generation-based chaotic encryption. Comput Syst Sci Eng. 2023;46(3):3209-3223 https://doi.org/10.32604/csse.2023.034185
IEEE Style
M.A. Duhayyim et al., "Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 3209-3223. 2023. https://doi.org/10.32604/csse.2023.034185



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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