A Novel Scheme to Optimize LSB Steganography Based on a Logistic Chaotic Map and Genetic Algorithm

Authors

Dena Abu Laila, Dep. of Information Technology, Hashemite University, JordanFollow
Ibrahim Moh’d obeidt, Dep. of Information Technology, Hashemite University, JordanFollow
Mohammad Aljaidi, Dep. of Computer Science, Zarqa University, JordanFollow
Mohammed Amin Almaiah, King Abdullah the II IT School, The University of Jordan, Amman 11942, JordanFollow
Muneer AlBourini, Faculty of Economics and Administrative Sciences, Zarqa University, JordanFollow
Qais Al-Na'amneh, Department of Cybersecurity and cloud computing, Applied Science Private University, JordanFollow
Ghassan Samara, Dep. of Computer Science, Zarqa University, Jordan
Rami Shehab, Vice-Presidency for Postgraduate Studies and Scientific Research, King Faisal University, Al-Ahsa 31982, Saudi ArabiaFollow
Khaled Momani, Associate Professor of Educational Administration, Faculty of Educational Sciences, Ajloun National University, Jordan

Abstract

Steganography has gained importance in various fields, including cloud security, internet banking, military applications, and medical imaging. Additionally, it is popular due to its many uses, becoming a hot research topic. The Least Significant Bit (LSB) approach is among the simplest ways to embed secret data in a cover image. We proposed a new scheme using LSB with the genetic algorithm GA to find the optimal solution permutation for embedding the pixel assortment of the image where data is to be concealed, and a chaotic genetic algorithm (CGA) to efficiently find the best crossover and mutation performance in the chromosomes. Random keys are created by using a 2D logistic map, and this key is used to encrypt gray images. So it is tough to find secret information. When comparing the PSNR, MSE, and SSIM metrics. The results of the analysis show that the recommended approach provides a useful level of security. With a focus on capacity and image quality where the 4-LSB value and baboon host image of size 512*512 with a maximum capacity up to 65k, the population size and iteration values for the genetic parameters are 50 and 50, respectively the PSNR reach 58.6, SSIM is 0.99995040 and MSE is 0.0896 Although, there is a negotiation between these metrics and keeping a better correspondence between them is still a big challenge.

Recommended Citation

Laila, Dena Abu; obeidt, Ibrahim Moh’d; Aljaidi, Mohammad; Almaiah, Mohammed Amin; AlBourini, Muneer; Al-Na'amneh, Qais; Samara, Ghassan; Shehab, Rami; and Momani, Khaled (2025) "A Novel Scheme to Optimize LSB Steganography Based on a Logistic Chaotic Map and Genetic Algorithm," Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 24.
DOI: https://doi.org/10.52866/2788-7421.1265
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/24