Cover
Vol. 20 No. 2 (2024)

Published: December 31, 2024

Pages: 45-58

Original Article

An Adaptive Steganography Insertion Technique Based on Cosine Transform

Taif Alobaidi 1 Wasfy Mikhael 2
1Department of Mobile Communications and Computing Engineering, College of Engineering, University of Information Technology and Communications (UOITC), Baghdad, Iraq
2Department of Electrical and Computer Engineering, University of Central Florida, Orlando, USA
History
  • Received: June 14, 2023
  • Revised: October 7, 2023
  • Accepted: October 20, 2023
  • Available Online: April 28, 2024
DOI

https://doi.org/10.37917/ijeee.20.2.4

Abstract

In the last couple decades, several successful steganography approaches have been proposed. Least Significant Bit (LSB) Insertion technique has been deployed due to its simplicity in implementation and reasonable payload capacity. The most important design parameter in LSB techniques is the embedding location selection criterion. In this work, LSB insertion technique is proposed which is based on selecting the embedding locations depending on the weights of coefficients in Cosine domain (2D DCT). The cover image is transformed to the Cosine domain (by 2D DCT) and predefined number of coefficients are selected to embed the secret message (which is in the binary form). Those weights are the outputs of an adaptive algorithm that analyses the cover image in two domains (Haar and Cosine). Coefficients, in the Cosine transform domain, with small weights are selected. The proposed approach is tested with samples from the BOSSbase, and a custom-built databases. Two metrics are utilized to show the effectiveness of the technique, namely, Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). In addition, human visual inspection of the result image is also considered. As shown in the results, the proposed approach performs better, in terms of (RMSE, and PSNR) than commonly employed truncation and energy based methods.

Keywords

References

  1. P. C. Mandal, I. Mukherjee, G. Paul, and B. Chatterji, “Digital image steganography: A literature survey,” In- formation sciences, 2022.
  2. A. A. AlSabhany, A. H. Ali, F. Ridzuan, A. Azni, and M. R. Mokhtar, “Digital audio steganography: System- atic review, classification, and analysis of the current state of the art,” Computer Science Review, vol. 38, p. 100316, 2020.
  3. M. A. Majeed, R. Sulaiman, Z. Shukur, and M. K. Hasan, “A review on text steganography techniques,” Mathemat- ics, vol. 9, no. 21, p. 2829, 2021.
  4. Y. Liu, S. Liu, Y. Wang, H. Zhao, and S. Liu, “Video steganography: A review,” Neurocomputing, vol. 335, pp. 238–250, 2019.
  5. S. Dhawan and R. Gupta, “Analysis of various data se- curity techniques of steganography: A survey,” Infor- mation Security Journal: A Global Perspective, vol. 30, no. 2, pp. 63–87, 2021.
  6. J. Fridrich and M. Goljan, “Practical steganalysis of dig- ital images: state of the art,” security and Watermarking of Multimedia Contents IV, vol. 4675, pp. 1–13, 2002.
  7. R. Sonar and G. Swain, “Steganography based on quo- tient value differencing and pixel value correlation,” CAAI Transactions on Intelligence Technology, vol. 6, no. 4, pp. 504–519, 2021.
  8. J. Kodovsky, J. Fridrich, and V. Holub, “Ensemble clas- sifiers for steganalysis of digital media,” IEEE Transac- tions on information forensics and security, vol. 7, no. 2, pp. 432–444, 2011.
  9. J. Davidson, C. Bergman, and E. Bartlett, “An artificial neural network for wavelet steganalysis,” in Mathematical Methods in Pattern and Image Analysis, vol. 5916, pp. 138–147, SPIE, 2005.
  10. A. Dehdar, A. Keshavarz, and N. Parhizgar, “Image steganalysis using modified graph clustering based ant colony optimization and random forest,” Multimedia Tools and Applications, vol. 82, no. 5, pp. 7401–7418, 2023.
  11. A. K. Sahu and M. Sahu, “Digital image steganography and steganalysis: A journey of the past three decades,” Open Computer Science, vol. 10, no. 1, pp. 296–342, 2020.
  12. S. Rahman, J. Uddin, H. U. Khan, H. Hussain, A. A. Khan, and M. Zakarya, “A novel steganography tech- nique for digital images using the least significant bit substitution method,” IEEE Access, vol. 10, pp. 124053– 124075, 2022.
  13. N. Subramanian, O. Elharrouss, S. Al-Maadeed, and A. Bouridane, “Image steganography: A review of the recent advances,” IEEE access, vol. 9, pp. 23409–23423, 2021.
  14. O. Elharrouss, N. Almaadeed, and S. Al-Maadeed, “An image steganography approach based on k-least signif- icant bits (k-lsb),” in 2020 IEEE International Confer- ence on Informatics, IoT, and Enabling Technologies (ICIoT), pp. 131–135, IEEE, 2020.
  15. R. J. Mstafa, K. M. Elleithy, and E. Abdelfattah, “A robust and secure video steganography method in dwt- dct domains based on multiple object tracking and ecc,” IEEE access, vol. 5, pp. 5354–5365, 2017.
  16. R. Das and T. Tuithung, “A novel steganography method for image based on huffman encoding,” in 2012 3rd Na- tional Conference on Emerging Trends and Applications in Computer Science, pp. 14–18, IEEE, 2012.
  17. T. Alobaidi and W. B. Mikhael, “Mixed nonorthogonal transforms representation for face recognition,” Circuits, Systems, and Signal Processing, vol. 38, pp. 1684–1694, 2019.
  18. M. Płachta, M. Krzemie´n, K. Szczypiorski, and A. Jan- icki, “Detection of image steganography using deep learning and ensemble classifiers,” Electronics, vol. 11, no. 10, 2022.
  19. A. S. Ansari, M. S. Mohammadi, and M. T. Parvez, “A multiple-format steganography algorithm for color images,” IEEE Access, vol. 8, pp. 83926–83939, 2020.
  20. W. Burger and M. J. Burge, Digital image processing: an algorithmic introduction using Java. Springer Science & Business Media, 2009.
  21. M. St´ephane, A wavelet tour of signal processing. Aca- demic press, 1999.
  22. R. Araya, “Enriching elementary school mathematical learning with the steepest descent algorithm,” Mathemat- ics, vol. 9, no. 11, p. 1197, 2021.
  23. R. Arun and M. Wasfy B., “Multitrans- form/multidimensional signal representation,” in Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on, pp. 1255–1258, IEEE, 1993.
  24. U. Sara, M. Akter, and M. S. Uddin, “Image quality as- sessment through fsim, ssim, mse and psnr—a compara- tive study,” Journal of Computer and Communications, vol. 7, no. 3, pp. 8–18, 2019.
  25. I.-H. Pan, K.-C. Liu, and C.-L. Liu, “Chi-square de- tection for pvd steganography,” in 2020 International 57 Symposium on Computer, Consumer and Control (IS3C), pp. 30–33, IEEE, 2020.