A Novel Development Adoptable to Robust Blind Watermarking Techniques for Secure Multimedia Authentication

Digital watermarking has become an essential technique for copyright protection, multimedia authentication, ownership verification, and secure information transmission. Conventional watermarking methods suffer from challenges such as low robustness against geometric attacks, poor imperceptibility, vulnerability to noise, and high computational complexity. This research paper proposes a novel robust blind watermarking framework based on a hybrid Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and chaotic encryption approach integrated with adaptive embedding strength optimization. The proposed method aims to improve robustness, imperceptibility, and security simultaneously while enabling blind extraction without requiring the original host image. The watermark is encrypted using chaotic logistic mapping before embedding into selected middle-frequency coefficients obtained from DWT-DCT decomposition. SVD-based singular value modification is employed for enhanced stability against signal processing and geometric attacks. Experimental analysis demonstrates improved Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation (NC) values compared with conventional methods. The proposed model is highly suitable for applications in copyright protection, medical image security, military communication, e-learning platforms, and secure multimedia transmission.

Blind watermarking, DWT, DCT, SVD, Chaotic encryption, Robust watermarking, Multimedia security, Copyright protection.

1. MATLAB documentation and image processing toolbox references were used for simulation methodology. [Google Scholar] [Crossref]

2. I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Transactions on Image Processing, vol. 6, no. 12, pp. 1673–1687, 1997. IEEE Xplore Paper [Google Scholar] [Crossref]

3. G. Bhatnagar and B. Raman, “A new robust reference watermarking scheme based on DWT-SVD,” Computer Standards & Interfaces, vol. 31, no. 5, pp. 1002–1013, 2009. ScienceDirect Article [Google Scholar] [Crossref]

4. E. Ganic and M. A. Eskicioglu, “Robust DWT-SVD domain image watermarking: Embedding data in all frequencies,” Proceedings of the Multimedia and Security Workshop, ACM, pp. 166–174, 2004. ACM Digital Library [Google Scholar] [Crossref]

5. K. A. Navas, A. M. Cheriyan, L. M. Nair, S. Archana, and M. Sasikumar, “DWT-DCT-SVD based watermarking,” International Conference on Communication System Software and Middleware, IEEE, 2008. IEEE Conference Paper [Google Scholar] [Crossref]

6. S. M. Mun, S. H. Nam, H. U. Jang, D. Kim, and H. K. Lee, “A robust blind watermarking using convolutional neural network,” arXiv preprint arXiv:1704.03248, 2017. arXiv Paper [Google Scholar] [Crossref]

7. M. Hamidi, M. El Haziti, H. Cherifi, and M. El Hassouni, “Hybrid blind robust image watermarking technique based on DFT-DCT and Arnold transform,” arXiv preprint arXiv:1911.00753, 2019. arXiv Research Article [Google Scholar] [Crossref]

8. A. Joseph and K. Anusudha, “Robust watermarking based on DWT SVD,” arXiv preprint arXiv:1309.2423, 2013. arXiv Paper [Google Scholar] [Crossref]

9. O. Noui and L. Noui, “A blind robust watermarking scheme based on SVD and circulant matrices,” arXiv preprint arXiv:1404.2952, 2014. arXiv Article [Google Scholar] [Crossref]

10. M. Zhang, Q. Zhang, and C. Zhou, “Robust digital image watermarking in DWT-SVD domain,” Lecture Notes in Computer Science, Springer, pp. 75–84, 2011. Springer Chapter [Google Scholar] [Crossref]

11. B. Nandeesh, L. S. Meti, and G. K. Manjunath, “A robust non-blind watermarking technique for color video based on combined DWT-DFT transforms and SVD technique,” International Journal of Information Technology and Computer Science, vol. 6, no. 9, pp. 59–65, 2014. MECS Journal Paper [Google Scholar] [Crossref]

12. B. Gopi and T. Sudha, “A robust watermarking scheme using DWT and SVD,” Asian Journal of Computer Science and Technology, vol. 8, no. S3, pp. 85–89, 2019. Journal Article [Google Scholar] [Crossref]

13. R. Liu and T. Tan, “An SVD-based watermarking scheme for protecting rightful ownership,” IEEE Transactions on Multimedia, vol. 4, no. 1, pp. 121–128, 2002. IEEE Multimedia Paper [Google Scholar] [Crossref]

14. F. Hartung and M. Kutter, “Multimedia watermarking techniques,” Proceedings of the IEEE, vol. 87, no. 7, pp. 1079–1107, 1999. IEEE Proceedings Paper [Google Scholar] [Crossref]

15. C.-T. Hsu and J.-L. Wu, “Hidden digital watermarks in images,” IEEE Transactions on Image Processing, vol. 8, no. 1, pp. 58–68, 1999. IEEE Journal Article [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet