Vol. 4 No. 1 (2026): SJESR - March 2026
Articles

A modified image watermarking method with compressed watermark based on DCT-SVD transforms

Published 2026-03-30

Keywords

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  • Array,
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How to Cite

A modified image watermarking method with compressed watermark based on DCT-SVD transforms. (2026). Samarra Journal of Engineering Science and Research, 4(1), 1-12. https://doi.org/10.65115/ft8tx819

Abstract

In this work, an enhanced watermarking method is proposed. The SVD transform is utilized to compress the watermark image, so the capacity, imperceptibility, and security are increased. The cover image is separated into blocks, and the embedding is carried out on the highest variance blocks to increase the imperceptibility of the method. The selected blocks are transformed using the Discrete Cosine Transform (DCT), a matrix including the DC coefficients is created, and transformed by the Singular Value Decomposition (SVD). The compressed watermark’s principal components are embedded into the singular values of the cover image. A robust method is obtained by using the DCT-SVD transforms. A watermark with different sizes can be embedded without association with the number of image blocks, while the size of the watermark in a similar method is restricted by the number of cover image blocks in rows and columns. High Peak Signal to Noise Ratio (PSNR) values are demonstrated by experimental results lying in the range (45.5-48.5) for the watermarked images of size 512*512 and a watermark of size 64*64, and it is possible to extract an acceptable quality watermark from the attacked image.

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References

  1. N. Muhammad and N. Bibi, “Digital image watermarking using partial pivoting lower and upper triangular decomposition into the wavelet domain,” IET Image Process., vol. 9, no. 9, pp. 795–803, 2015, doi: 10.1049/iet-ipr.2014.0395.
  2. Q. Su et al., “New Rapid and Robust Color Image Watermarking Technique in Spatial Domain,” IEEE Access, vol. 7, pp. 30398–30409, 2019, doi: 10.1109/ACCESS.2019.2895062.
  3. D. G. Savakar and A. Ghuli, “Robust Invisible Digital Image Watermarking Using Hybrid Scheme,” Arab. J. Sci. Eng., vol. 44, no. 4, pp. 3995–4008, 2019, doi: 10.1007/s13369-019-03751-8.
  4. Abraham and V. Paul, “An imperceptible spatial domain color image watermarking scheme,” J. King Saud Univ. - Comput. Inf. Sci., vol. 31, no. 1, pp. 125–133, 2019, doi: 10.1016/j.jksuci.2016.12.004.
  5. H. K. Singh and A. K. Singh, “Digital image watermarking using deep learning,” Multimed. Tools Appl., vol. 83, no. 1, pp. 2979–2994, 2024, doi: 10.1007/s11042-023-15750-x.
  6. R. Singh, L. I. Izhar, I. Elamvazuthi, A. Ashok, S. Aole, and N. Sharma, “Efficient Watermarking Method Based on Maximum Entropy Blocks Selection in Frequency Domain for Color Images,” IEEE Access, vol. 10, pp. 52712–52723, 2022, doi: 10.1109/ACCESS.2022.3174964.
  7. M. Begum, J. Ferdush, and M. S. Uddin, “A Hybrid robust watermarking system based on discrete cosine transform, discrete wavelet transform, and singular value decomposition,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5856–5867, 2022, doi: 10.1016/j.jksuci.2021.07.012.
  8. L. Lidyawati, A. R. Darlis, L. Jambola, L. Kristiana, and R. R. Jayandanu, “Digital watermarking image using three-level discrete wavelet transform under attacking noise,” Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 231–238, 2022, doi: 10.11591/eei.v11i1.3565.
  9. J. Liu, Z. Li, Q. Miao, P. Qi, and D. Wang, “Adaptive bistable stochastic resonance based blind watermark extraction in discrete cosine transform domain,” IET Image Process., vol. 17, no. 14, pp. 4028–4043, 2023, doi: 10.1049/ipr2.12916.
  10. D. Liu, D. Liu, B. Wang, and P. Zheng, “Hybrid domain digital watermarking scheme based on improved differential evolution algorithm and singular value block embedding,” IET Image Process., vol. 17, no. 8, pp. 2516–2536, 2023, doi: 10.1049/ipr2.12814.
  11. M. Saiful Islam, M. A. Ullah, and J. P. Dhar, “An imperceptible & robust digital image watermarking scheme based on DWT, entropy and neural network,” Karbala Int. J. Mod. Sci., vol. 5, no. 1, 2019, doi: 10.33640/2405-609X.1068.
  12. F. Ernawan and D. Ariatmanto, “Image watermarking based on integer wavelet transform-singular value decomposition with variance pixels,” International Journal of Electrical and Computer Engineering, vol. 9, no. 3, pp. 2185–2195, 2019. doi: 10.11591/ijece.v9i3.pp2185-2195.
  13. F. Ernawan, D. Ariatmanto, and A. Firdaus, “An Improved Image Watermarking by Modifying Selected DWT-DCT Coefficients,” IEEE Access, vol. 9, pp. 45474–45485, 2021, doi: 10.1109/ACCESS.2021.3067245.
  14. R. Singh, A. Ashok, and M. Saraswat, “Optimised robust watermarking technique using CKGSA in DCT-SVD domain,” IET Image Processing, vol. 14, no. 10, pp. 2052–2063, 2020, doi: 10.1049/iet-ipr.2019.1059.
  15. Y. AL-Nabhani, H. A. Jalab, A. Wahid, and R. M. Noor, “Robust watermarking algorithm for digital images using discrete wavelet and probabilistic neural network,” J. King Saud Univ. - Comput. Inf. Sci., vol. 27, no. 4, pp. 393–401, 2015, doi: 10.1016/j.jksuci.2015.02.002.
  16. S. Liu, Z. Pan, and H. Song, “Digital image watermarking method based on DCT and fractal encoding,” IET Image Process., vol. 11, no. 10, pp. 815–821, 2017, doi: 10.1049/iet-ipr.2016.0862.
  17. J. Liu et al., “An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain,” IEEE Access, vol. 7, pp. 80849–80860, 2019, doi: 10.1109/ACCESS.2019.2915596.
  18. W. Wu, Y. Dong, and G. Wang, “Image robust watermarking method based on DWT-SVD transform and chaotic map,” Complexity, vol. 2024, Art. no. 6618382, pp. 1–18, 2024, doi: 10.1155/2024/6618382.
  19. M. Fahim Hossain Saikat, M. A. M. Provath, K. Deb, P. K. Dhar, and T. Shimamura, “Deep Learning-Based Image Watermarking Using Catalan Transform and Non-Negative Matrix Factorization,” IEEE Access, vol. 13, no. April, pp. 68995–69020, 2025, doi: 10.1109/ACCESS.2025.3558121.
  20. S. K. Ahmed and S. N. M. Al-Faydi, “A Novel Invisible Image Watermarking Based On The Relation Between The Selected DCT Coefficients,” 2024 2nd Int. Conf. Softw. Eng. Inf. Technol. ICoSEIT 2024,Bandung, Indonesia, 2024, pp. 108–113, doi: 10.1109/ICoSEIT60086.2024.10497486.
  21. Shubuh, Syafiqul; Ernawan, Ferda; Amrullah, Agit; and Wahyu, Prajanto, "Robust ImageWatermarking Based on IWT-DCT-SVD for Copyright Protection," Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 4, pp. 27-35,2024, doi.org/10.52866/2788-7421.1201
  22. Z. Zhou, J. Zhu, Y. Su, M. Wang, and X. Sun, “Geometric correction code-based robust image watermarking,” IET Image Processing, vol. 17, no. 13, p.p. 3660-3669,2021, doi: 10.1049/ipr2.12143.
  23. T. Sutojo, E. H. Rachmawanto, and C. A. Sari, “Fast and efficient image watermarking algorithm using discrete tchebichef transform,” in 2017 5th International Conference on Cyber and IT Service Management (CITSM), Denpasar, Indonesia,2017, pp. 1–5.
  24. S. E. Tsai and S. M. Yang, “A fast DCT algorithm for watermarking in digital signal processor,” Math. Probl. Eng., vol. 2017, no. 1, 7 pages, 2017, doi.org/10.1155/2017/7401845.
  25. R. M. Al-Saleem, Y. A. Ghani, and S. A. Shawkat, “Improvement of Image Compression by Changing the Mathematical Equation Style in Communication Systems,” Int. J. Digit. Multimed. Broadcast., vol. 2022, no. 1, 7 pages, 2022, doi.org/10.1155/2022/3231533.
  26. R. A. Sadek, “SVD Based Image Processing Applications : State of The Art, Contributions and Research Challenges,” International Journal of Advanced Computer Science and Applications, vol. 3, no.7, p.p 26-34, 2012, doi: 10.14569/IJACSA.2012.030703.
  27. M. Ali, C. W. Ahn, and M. Pant, “A robust image watermarking technique using SVD and differential evolution in DCT domain,” Optik (Stuttg), vol. 125, no. 1, pp. 428–434, 2014, doi: 10.1016/j.ijleo.2013.06.082.
  28. E. Ganic, N. Zubair, and A. M. Eskicioglu, “An optimal watermarking scheme based on singular value decomposition,” in Proceedings of the IASTED international conference on communication, network, and information security, New York, USA ,2003.
  29. J. M. Guo and H. Prasetyo, “False-positive-free SVD-based image watermarking,” J. Vis. Commun. Image Represent., vol. 25, no. 5, pp. 1149–1163, 2014, doi: 10.1016/j.jvcir.2014.03.012.
  30. S. K. Ahmed, “A Modified Method For Selecting Singular Values In Image Compression Using Singular Value Decomposition,” Journal of Engineering Science and Technology, vol. 17, no. 4, pp. 2556–2566, 2022.