Bioinspired Optimization Algorithms of Steganography: A Review
DOI:
https://doi.org/10.21928/uhdjst.v10n1y2026.pp88-100Keywords:
Bioinspired, Optimization Techniques, Payload Capacity, Steganography, Least Significant Bit, Spatial Domain, Data HidingAbstract
Hiding data inside images is a viable alternative to cryptographic approaches for data security. Steganography algorithms protect the confidentiality of communications hidden behind cover images and provide clandestine communication channels. The main problem in the development of data hiding techniques is to achieve a balance between indicators of embedding quality, including imperceptibility, capacity, and robustness. A practical approach to solving this issue is the use of bio-inspired optimization algorithms. This review presents how combining bio-inspired algorithms with spatial domain techniques enhances the effectiveness of data embedding within digital images. It also reviews traditional methods, most of which utilize the least significant bit substitution method, and their associated limitations in imperceptibility, payload capacity, and robustness. The review analyzes several bio-inspired optimization approaches and their effectiveness in optimizing embedding positions to improve imperceptibility, security, and capacity. The paper highlights the benefits of bio-inspired strategies for reducing the drawbacks of spatial domain steganography, namely for improving concealed data capacity while keeping visual quality better. The findings demonstrate that bio-inspired techniques offer a practicable solution for enhancing both security and effectiveness of hidden systems by addressing limitations of traditional techniques and facilitating the development of more potent concealment of data solutions.
References
Z. K. J. Jasim and S. Kurnaz. “An improved image steganography security and capacity using ant colony algorithm optimization”. Computers, Materials and Continua., vol. 80, no. 3, pp. 4643-4662, 2024.
A. Cheddad, J. Condell, K. Curran and P. Mc Kevitt. “Digital image steganography: Survey and analysis of current methods”. Signal Processing, vol. 90, no. 3, pp. 727-752, 2010.
C. K. Chan and L. M. Cheng. “Hiding data in images by simple LSB substitution”. Pattern Recognition, vol. 37, no. 3, pp. 469-474, 2004.
T. Sakakura and A. Hayashi. “A Simple Detection Scheme of LSB Steganography Based on Statistics of Image Difference Signal”. ISITA/ISSSTA 2010 - 2010 International Symposium on Information Theory and Its Applications, pp. 320-325, 2010.
C. H. Yang. “Inverted pattern approach to improve image quality of information hiding by LSB substitution”. Pattern Recognition, vol. 41, no. 8, pp. 2674-2683, 2008.
M. Khodaei and K. Faez. “Image hiding by using genetic algorithm and LSB substitution. In: “ Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)”. vol. 6134. Springer Science+Business Media, Germany, pp. 404-411, 2010.
V. Gnanalakshmi and G. Indumathi. “A review on image steganographic techniques based on optimization algorithms for secret communication”. Multimedia Tools and Applications, vol. 82, no. 28, pp. 44245-44258, 2023.
J. T. Jackson, G. H. Gunsch, R. L. Claypoole and G. B. Lamont. “Blind steganography detection using a computational immune system : A work in progress”. International Journal of Digital Evidence, vol. 4, no. 1, pp. 1-19, 2003.
J. T. Jackson, G. H. Gunsch, R. L. Claypoole, and G. B. Lamont. “Novel Steganography Detection Using an Artificial Immune System Approach”. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 139-145, 2003.
S. P. Maity, M. K. Kundu and P. K. Nandi. “Genetic algorithm for optimal imperceptibility in image communication through noisy channel”. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3316, pp. 700-705, 2004.
M. N. Wu, M. H. Lin and C. C. Chang. “A LSB substitution oriented image hiding strategy using genetic algorithms”. In: Lecture Notes in Computer Science. (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3309, pp. 219-229, 2004.
X. Li and J. Wang. “A steganographic method based upon JPEG and particle swarm optimization algorithm”. Information Sciences Journal, vol. 177, no. 15, pp. 3099-3109, 2007.
C. S. Hsu and S. F. Tu. “Finding Optimal LSB Substitution using Ant Colony Optimization Algorithm”. In: 2nd 18th International Conference on Communication Software and Networks, ICCSN 2010, pp. 293-297, 2010.
A. L. Brazil, A. Sanchez, A. Conci and N. Behlilovic. “Hybridizing Genetic Algorithms and Path Relinking for Steganography”. In: Proceedings Elmar - International Symposium Electronics in Marine, pp. 285-288, 2011.
F. H. Rabevohitra and J. Sang. “Using PSO Algorithm for Simple LSB Substitution Based Steganography Scheme in DCT Transformation Domain”. In: Lecture Notes in Computer Science. (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6728, no. Part 1, pp. 212-220, 2011.
C. C. Chang, T. S. Chen, and L. Z. Chung. “A steganographic method based upon JPEG and quantization table modification”. Information Sciences, vol. 141, no. 1-2, pp. 123-138, 2002.
I. C. Lin, Y. Bin Lin and C. M. Wang. “Hiding data in spatial domain images with distortion tolerance”. Computer Standards and Interfaces, vol. 31, no. 2, pp. 458-464, 2009.
Y. H. Huang, C. C. Chang and C. Y. Wu. “A DNA-based data hiding technique with low modification rates”. Multimedia Tools and Applications, vol. 70, no. 3, pp. 1439-1451, 2014.
P. Bedi, R. Bansal and P. Sehgal. “Using PSO in a spatial domain based image hiding scheme with distortion tolerance”. Computers and Electrical Engineering, vol. 39, no. 2, pp. 640-654, 2013.
A. H. Mohsin, A. A. Zaidan, B. B. Zaidan, O. S. Albahri, A. S. Albahri and M. A. Alsalem. “New method of image steganography based on particle swarm optimization algorithm in spatial domain for high embedding capacity”. IEEE Access, vol. 7, pp. 168994-169010, 2019.
A. Banharnsakun. “Artificial bee colony approach for enhancing LSB based image steganography”. Multimedia Tools and Applications, vol. 77, no. 20, pp. 27491-27504, 2018.
O. M. Alade, E. A. Amusan, O. T. Adedeji, and O. O. Alo. “Image steganography using pixel value differencing (PVD) technique based on firefly algorithm,” J. Sci. Res. Reports, vol. 27, no. 7, pp. 80–86, 2021.
Z. H. Wang, C. C. Chang and M. C. Li. “Optimizing least-significant-bit substitution using cat swarm optimization strategy”. Information Sciences, 192, 98-108, 2012.
C. Map, S. Makhija, K. Singh, K. Sharma, R. Stego-key, and C. Map, “Robust Stego-Key Directed LSB Substitution Scheme based upon Cuckoo Search and Chaotic Map,” 2018.
M. A. Hameed, O. A. Abdel-Aleem, and M. Hassaballah. “A secure data hiding approach based on least-significant-bit and nature-inspired optimization techniques”. Journal of Ambient Intelligence and Humanized Computing., vol. 14, no. 5, pp. 4639-4657, 2023.
R. Z. Wang, C. F. Lin and J. C. Lin. “Image hiding by optimal LSB substitution and genetic algorithm”. Pattern Recognition, vol. 34, no. 3, pp. 671-683, 2001.
S. Basu, A. Debnath, A. Basu and T. S. Das. “An image data hiding technique using differential evolution”. Multimedia Tools and Applications, vol. 81, no. 28, pp. 39995-40012, 2022.
S. D. Ahmadi and H. Sajedi. “Image Steganography with Artificial Immune System”. In: 7th Artificial Intelligence and Robotics IRANOPEN 2017, pp. 45-50, 2017.
O. Y. Abdulhammed, P. J. Karim, D. R. Arif, T. S. Ali, A. O. Abdalrahman and A. A. Saffer. “A secure image steganography using shark smell optimization and edge detection technique”. Kurdistan Journal of Applied Research, vol. 7, no. 2, pp. 11-25, 2022.
A. Jaradat, E. Taqieddin, and M. Mowafi. “A high-capacity image steganography method using chaotic particle swarm optimization”. Security and Communication Networks, vol. 2021, p. 11, 2021.
A. Colorni, M. Dorigo and V. Maniezzo. “Distributed Optimization by Ant Colonies”. Proceedings ECAL. Elsevier Publishing, Amsterdam, pp. 134-142, 1991.
M. Dorigo and T. Stützle. “Ant Colony Optimization: Overview and Recent Advances”. Vol. 272. Université Libre de Bruxelles, Belgium, 2019.
D. Karaboga. “An Idea Based on Honey Bee Swarm For Numerical Optimization”. Technical Report - TR06, 2005.
M. A. Al-Abaji. “A literature review of cuckoo search algorithm”. Journal of Education and Practice, vol. 11, no. 8, pp. 1-8, 2020.
B. T. Ahmed and O. Y. Abdulhameed. “Fingerprint authentication using shark smell optimization algorithm”. UHD Journal of Science and Technology, vol. 4, no. 2, pp. 28-39, 2020.
N. A. Kamarzaman, S. I. Sulaiman, and I. R. Ibrahim, “Adaptive Mechanism For Enhanced Performance Of Shark Smell Optimization,” J. Electr. Electron. Syst. Res., vol. 18, 2021.
M. Soleimanpour and S. Talebi. “A novel technique for steganography method based on improved genetic algorithm optimization in spatial Domain”. Iranian Journal of Electrical and Electronic Engineering, vol. 9, no. 2, pp. 67-75, 2013.
S. Arivazhagan, W. S. L. Jebarani, S. Ananthi Roy and E. Amrutha. “Improving quality of stego images through dithering techniques for pixel pair matching steganographic schemes”. International Journal of Engineering and Advanced Technology, vol. 8, no. 6, pp. 4923-4931, 2019.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Salam Hussein Ahmed, Omar Younis Abdulhameed
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
