A Novel Watermarking Method with Resistance to Image Manipulations via Dual-Tree Complex Wavelet Domain

Öz

A new digital image watermarking method based on dual-tree complex wavelet transform is proposed. The proposed method is shown to be robust to geometric and non-geometric modifications performed on the watermarked image. The complex wavelet domain for watermark insertion gives rise to robustness for non-geometric modifications, while the image normalization provides robustness against geometric modifications. First, the original image is normalized. Then, a watermark is generated such that the capacity is more than one bit according to the properties of the human visual system, and it is added to the three detail subbands coefficients of the normalized image at the first decomposition level in the complex wavelet domain. Finally, successive applications of the inverse complex wavelet transform and the inverse normalization give the watermarked image. Computer simulations show that the method is robust to numerous image manipulations and is superior to the existing digital image watermarking approaches. However, the implementation of the complex wavelet transform is computationally costly. Hence, dual tree implementation is used to reduce computational complexity in the experiments.

Anahtar Kelimeler

• Digital image watermarking
• Image normalization
• DT-CWT

Kaynakça

1. Alghoniemy, M., & Tewfik, A. H. (2004). Geometric invariance in image watermarking. IEEE Transactions on Image Processing, 13(2), 145–153. https://doi.org/10.1109/TIP.2004.823831
2. Barni, M., Bartolini, F., & Piva, A. (2001). Improved wavelet-based watermarking through pixel-wise masking. IEEE Transactions on Image Processing, 10(5), 783–791. https://doi.org/10.1109/83.918570
3. Begum, M., & Uddin, M. S. (2020). Digital image watermarking techniques: A review. Information, 11(2), 110. https://doi.org/10.3390/info11020110
4. Dash, A., Naik, K., & Priyadarshini, P. (2025). Robust digital image watermarking using hybrid optimization technique. In Intelligent Computing Techniques and Applications (pp. 300–305).
5. Dong, P., & Galatsanos, N. P. (2002). Affine transformation resistant watermarking based on image normalization. In Proceedings of IEEE International Conference on Image Processing (Vol. 3, pp. 489–492).
6. Eldaoushy, A. F., Desouky, M. I., El-Dolil, S. A., El-Fishawy, A. S., & El-Samie, F. E. A. (2023). Efficient hybrid digital image watermarking. Journal of Optics, 52(4), 2224-2238. https://doi.org/10.1007/s12596-023-01144-7
7. Guo, L., & Zhou, J. (2010). A new robust digital watermarking based on complex wavelet transform. In Proceedings of the International Conference on E-Business and E-Government (ICEE) (pp. 3529–3532).
8. Jia, Z., Fang, H., & Zhang, W. (2021). MBRS: Enhancing robustness of DNN-based watermarking by mini-batch of real and simulated JPEG compression. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 41–49).

9. Kazan, S. (2009). Digital image watermarking methods using moment based normalization (Master’s thesis). Institute of Science, Sakarya.
10. Kim, H. S., & Lee, H. K. (2003). Invariant image watermark using Zernike moments. IEEE Transactions on Circuits and Systems for Video Technology, 13(8), 766–775. https://doi.org/10.1109/TCSVT.2003.815955
11. Kumar, L., Singh, K. U., & Kumar, I. (2023). A comprehensive review on digital image watermarking techniques. In 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) (pp. 737–743).
12. Lawton, W. (1993). Applications of complex valued wavelet transforms to subband decomposition. IEEE Transactions on Signal Processing, 41(12), 3566–3568.
13. Liu, J., & She, K. (2010). Robust image watermarking using dual tree complex wavelet transform based on human visual system. In Proceedings of the International Conference on Image Analysis and Signal Processing (IASP) (pp. 675–679).
14. Loo, P., & Kingsbury, N. (2000). Digital watermarking using complex wavelets. IEEE Transactions on Signal Processing, 2(3), 29–32.
15. O’Ruanaidh, J. J. K., & Pun, T. (1998). Rotation, scale, and translation invariant spread spectrum digital image watermarking. Signal Processing, 66(3), 303–317.
16. Pavan, A. C., & Somashekara, M. T. (2023). An overview on research trends, challenges, applications and future direction in digital image watermarking. International Research Journal on Advanced Science Hub, 5(1). http://dx.doi.org/10.47392/irjash.2023.002
17. Reza, S., Hosen, M. A., Rahman, A., & Galib, S. M. (2025). A hybrid approach to digital image watermarking integrating DWT, DFT, and genetic algorithm. In International Conference on Artificial Intelligence and Smart Energy (pp. 484–496).
18. Rothe, I., Susse, H., & Voss, K. (1996). The method of normalization to determine invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(4), 366–376.
19. Selesnick, I. W., Baraniuk, R. G., & Kingsbury, N. G. (2005). The dual-tree complex wavelet transform. IEEE Signal Processing Magazine, 22(6), 123–151.
20. Shen, D., & Ip, H. H. S. (1997). Generalized affine invariant image normalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5), 431–440.
21. Wang, X., Hou, L., & Wu, J. (2008). A feature-based robust digital image watermarking against geometric attacks. Image and Vision Computing, 26(7), 980–989.
22. Wood, J. (1996). Invariant pattern recognition: A review. Pattern Recognition, 29(1), 1–17.
23. Yang, H., Jiang, X., & Kot, A. C. (2010). Image watermarking using dual-tree complex wavelet by coefficients swapping and group of coefficients quantization. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) (pp. 1673–1678).
24. Zhang, K. A., Xu, L., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Robust invisible video watermarking with attention. arXiv, 1909.01285. https://doi.org/10.48550/arXiv.1909.01285
25. Zheng, D., Zhao, J., & El Saddik, A. (2003). RST-invariant digital image watermarking based on log-polar mapping and phase correlation. IEEE Transactions on Circuits and Systems for Video Technology, 13(8), 753–765.
26. Zhu, J., Kaplan, R., Johnson, J., & Fei-Fei, L. (2018). HiDDeN: Hiding data with deep networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 657–672).