The rapid expansion of digital communication and information exchange has increased the need for secure data transmission. Image steganography the process of hiding secret information within images provides a powerful solution for covert communication. This study presents a comparative analysis of three spatial-domain techniques: Least Significant Bit (LSB), Pixel Value Differencing (PVD), and Exploiting Modification Direction (EMD), followed by a hybrid embedding model that combines their advantages. Each method was evaluated using standard test images such as Lena, Baboon, and Peppers, and assessed based on Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), histogram similarity, and Pairs of Values (PoV) steganalysis. Results demonstrate that LSB achieves the highest embedding capacity (≈1 bit/pixel) but is vulnerable to statistical attacks. PVD provides a good balance between capacity and imperceptibility, while EMD ensures stronger statistical invisibility with minimal pixel modification. The proposed hybrid method adaptively selects embedding strategies based on local image variance, achieving PSNR above 48 dB, SSIM over 0.99, and a PoV detection rate below 1.5%. These results confirm the hybrid model’s robustness and imperceptibility. This research highlights that integrating multiple steganographic techniques enhances overall performance and security, offering a promising framework for secure image-based communication and data protection.

This study presented a comprehensive comparative analysis of three major spatial domain steganographic techniques Least Significant Bit (LSB), Pixel Value Differencing (PVD), and Exploiting Modification Direction (EMD) alongside a hybrid embedding model designed to integrate their strengths. Through extensive experiments using benchmark grayscale images, the hybrid method demonstrated significant superiority across all performance dimensions. The hybrid model consistently achieved PSNR values above 48 dB and SSIM scores exceeding 0.99, confirming its ability to maintain high visual fidelity and imperceptibility. Under moderate image distortions such as JPEG compression and Gaussian noise, it preserved extraction accuracy above 95%, outperforming traditional methods by a wide margin. In terms of security, the Pairs of Values (PoV) analysis reported a detection rate of only 1.3%, highlighting strong resistance to statistical steganalysis. While the computational cost increased slightly due to adaptive region analysis, this overhead was acceptable given the improvements in robustness, capacity, and invisibility. Overall, the proposed hybrid algorithm offers a balanced, adaptive, and secure framework for data hiding in digital images. Its ability to dynamically adjust to image complexity makes it a promising solution for future applications in digital watermarking, secure communication, and multimedia data protection.

1. Johnson, N.F. and S. Jajodia, Exploring steganography: Seeing the unseen. computer, 2008. 31(2): p. 26-34. 2. Fridrich, J., Steganography in digital media: principles, algorithms, and applications. 2009: Cambridge university press. 3. Wu, D.-C. and W.-H. Tsai, A steganographic method for images by pixel-value differencing. Pattern recognition letters, 2003. 24(9-10): p. 1613-1626. 4. Zhang, X. and S. Wang, Efficient steganographic embedding by exploiting modification direction. IEEE Communications letters, 2006. 10(11): p. 781-783. 5. Din, R., et al., Review on steganography methods in multi-media domain. 2019. 8(1.7): p. 288-292. 6. Alaa Jabbar, Q. and A. Farah Qasim Ahmed, History of Image Digital Formats Using in Information Technology. QALAAI ZANIST SCIENTIFIC JOURNAL, 2021. 6(2): p. 1098-1112. 7. Alaa Jabbar, Q., D. Roshidi, and A. Farah Qasim Ahmed, Extended Method of Least Significant Bits on Colour Images in Steganography. QALAAI ZANIST SCIENTIFIC JOURNAL, 2024. 9(3): p. 1146-1158. 8. Utama, S., et al., Analytical and Empirical Insights into Wireless Sensor Network Longevity: The Role MAC Protocols and Adaptive Strategies. Journal of Advanced Research in Computing and Applications, 2024. 36(1): p. 52-60. 9. Din, R., A.J.J.B.o.E.E. Qasim, and Informatics, Steganography analysis techniques applied to audio and image files. 2019. 8(4): p. 1297–1302. 10. Devi, K.J., A sesure image steganography using LSB technique and pseudo random encoding technique. 2013, National Institute of Technology-Rourkela. 11. Kaur, G. and A. Kochhar, A steganography implementation based on LSB & DCT. International Journal for Science and Emerging Technologies with Latest Trends, 2012. 4(1): p. 35-41. 12. Dasgupta, K., J. Mandal, and P. Dutta, Hash based least significant bit technique for video steganography (HLSB). International Journal of Security, Privacy and Trust Management (IJSPTM), 2012. 1(2): p. 1-11. 13. Chan, C.-K. and L.-M. Cheng, Hiding data in images by simple LSB substitution. Pattern recognition, 2004. 37(3): p. 469-474. 14. Dumitrescu, S., X. Wu, and Z. Wang. Detection of LSB steganography via sample pair analysis. in International Workshop on Information Hiding. 2002. Springer. 15. Qasim, A.J., et al., Review on techniques and file formats of image compression. 2020. 9(2): p. 602–610. 16. Din, R., et al., Exploring Steganographic Techniques for Enhanced Data Protection in Digital Files. International Journal of Advanced Research in Computational Thinking and Data Science, 2024. 1(1): p. 1-9. 17. Din, R., et al., Innovative Machine Learning Applications in Non-Revenue Water Management: Challenges and Future Solution. Semarak International Journal of Machine Learning, 2024. 1(1): p. 1-10. 18. Thangadurai, K. and G.S. Devi. An analysis of LSB based image steganography techniques. in Computer Communication and Informatics (ICCCI), 2014 International Conference on. 2014. IEEE. 19. Alyousuf, F.Q.A., R. Din, and A.J. Qasim, Analysis review on spatial and transform domain technique in digital steganography. Bulletin of Electrical Engineering and Informatics, 2020. 9(2): p. 573-581. 20. Qasim, A.J., R. Din, and F.Q.A. Alyousuf, Review on techniques and file formats of image compression. Bulletin of Electrical Engineering and Informatics, 2020. 9(2): p. 602-610. 21. Qasim Almaliki, A.J., et al., Application of the Canny Filter in Digital Steganography. Journal of Advanced Research in Computing and Applications, 2024. 35(1): p. 21-30. 22. Roshidi Din, O.G., Alaa Jabbar Qasim, Analytical Review on Graphical Formats Used in Image Steganographic Compression. Indonesian Journal of Electrical Engineering and Computer Science, 2018. Vol 12, No 2: p. pp. 441~446. 23. Cheddad, A., et al., Digital image steganography: Survey and analysis of current methods. Signal processing, 2010. 90(3): p. 727-752.

© 2025 International Journal of Engineering and Techniques (IJET).

Digital Object Identifier (DOI)

IJET assigns a unique DOI to every accepted article via Zenodo for persistent linking and citation. Your article’s DOI: https://doi.org/{{doi}}

Open DOI About Zenodo DOI

Close