Securing Data Using Improved Cryptography and Steganography Techniques
Authors
Department of Computer Science Federal College of Education (Special), Oyo (Nigeria)
Department of Cybersecurity, Ajayi Crowther University, Oyo. (Nigeria)
Article Information
DOI: 10.51244/IJRSI.2026.1313CS026
Subject Category: Computer Science
Volume/Issue: 13/13 | Page No: 332-343
Publication Timeline
Submitted: 2026-08-24
Accepted: 2026-08-29
Published: 2026-09-14
Abstract
The rapid growth of digital communication and information exchange has increased the vulnerability of data to unauthorized access, interception, modification, and other cyber threats, making effective data security increasingly important. Conventional cryptographic techniques protect the content of data but may expose the existence of the communication, while steganography conceals the existence of information but may not provide sufficient protection if the hidden data is discovered or extracted. This study proposes an improved hybrid data security model that combines enhanced cryptographic and steganographic techniques to provide multiple layers of protection. In the proposed approach, data is first encrypted using an improved cryptographic technique to ensure confidentiality and prevent unauthorized access. The encrypted data is then concealed within a digital image using an enhanced Least Significant Bit (LSB) steganographic technique, thereby reducing the likelihood of detection during transmission or storage. The performance of the proposed model is evaluated using security strength, embedding capacity, Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and resistance to common attacks and steganalysis. The proposed approach is expected to improve data confidentiality and concealment while maintaining high visual quality of the stego-image and adequate data embedding capacity. The study demonstrates that integrating improved cryptography with steganography can provide stronger data protection than using either technique independently. The proposed model has potential applications in secure communication, financial transactions, healthcare information systems, military communication, cloud environments, and confidential data storage and transmission.
Keywords
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References
1. Hashmi, M., Yamin, M., & Yayilgan, S. Y. (2024). Artificial intelligence and information security: Emerging challenges and opportunities in modern cybersecurity systems. International Journal of Information Security, 23(2), 145–162. [Google Scholar] [Crossref]
2. Haverkamp, I., & Sarmah, D. K. (2024). Evaluating the merits and constraints of cryptography-steganography fusion: A systematic analysis. International Journal of Information Security, 23, 2607–2635. https://doi.org/10.1007/s10207-024-00853-9 [Google Scholar] [Crossref]
3. Kombrink, M. H., Geradts, Z. J. M. H., & Worring, M. (2024). Image Steganography Approaches and Their Detection Strategies: A Survey. ACM Computing Surveys, 57(2), Article 33. https://doi.org/10.1145/3694965 [Google Scholar] [Crossref]
4. Rezaei, S., & Javadpour, A. (2024). Bio-Inspired algorithms for secure image steganography: Enhancing data security and quality in data transmission. Multimedia Tools and Applications, 83, 82247–82280. [Google Scholar] [Crossref]
5. Rajabi-Ghaleh, A., Rahmani, M., & Hosseini, S. (2024). Enhancing secure communication through RSA-AES cryptographic integration and image steganography techniques. International Journal of Computer Applications, 186(7), 15–28. [Google Scholar] [Crossref]
6. Rezaei, M., & Javadpour, N. (2024). Bio-inspired optimization algorithms for secure image steganography and information hiding. Multimedia Tools and Applications, 83(12), 34567–34589. [Google Scholar] [Crossref]
7. Swanzy, P., Ansong, E., & Abukari, A. (2024). Data security frameworks for cloud computing environments: Ensuring confidentiality, integrity and availability of information. Journal of Information Assurance and Security, 19(3), 201–215. [Google Scholar] [Crossref]
8. Xiao, L., Chen, L., Lai, J., & Liu, S. (2024). Deep learning-based image steganography using generative adversarial networks for secure information hiding. Expert Systems with Applications, 245, 122–138. [Google Scholar] [Crossref]
9. Ali, A., et al. (2024). Optimize Image Steganography Based on Distinction Disparity Value and HMPSO to Ensure Confidentiality and Integrity. Journal of Computer Networks and Communications. https://doi.org/10.1155/2024/2516567 [Google Scholar] [Crossref]
10. El-Hady, M., Abbas, M. H., Khanday, F. A., Said, L. A., & Radwan, A. G. (2024). DISH: Digital image steganography using stochastic-computing with high-capacity. Multimedia Tools and Applications, 83, 66033–66048. https://doi.org/10.1007/s11042-023-17998-9 [Google Scholar] [Crossref]
11. Muralidharan, T., Cohen, A., Cohen, A., & Nissim, N. (2022). The infinite race between steganography and steganalysis in images. Signal Processing, 199, 108711. https://doi.org/10.1016/j.sigpro.2022.108711 [Google Scholar] [Crossref]
12. Şahin, F., Çevik, T., & Takaoğlu, M. (2021). Review of the literature on the steganography concept. International Journal of Computer Applications, 183(2), 38–46. https://doi.org/10.5120/ijca2021921298 [Google Scholar] [Crossref]
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