Robust watermark based on Schur decomposition and dynamic weighting factors

• Published in: The Visual computer Vol. 40; no. 5; pp. 3249 - 3269
• Main Authors: Nasser, Reem, Abouelseoud, Yasmine, Mikhail, Mervat
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Graphics; Computer Science; Copy protection; Copyright; Decomposition; Digital watermarks; Embedding; Fourier transforms; Image Processing and Computer Vision; Logos; Multimedia; Optimization techniques; Original Article; Robustness; Scaling factors; Statistical analysis; Tradeoffs; Trajectory optimization; Visibility; Watermarking; Wavelet transforms; Weighting; Discrete wavelet transform (DWT); Friedman’s test; Schur decomposition; Image watermarking; Singular value decomposition (SVD); Pattern search optimization (PS)
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-023-03028-0

Since multimedia data are now more widely distributed in digital form and easier to copy and change, copyright protection has become an essential need. One of the most common copyright protection techniques is watermarking. Invisibility and robustness are crucial aspects of watermarking; however, many of the currently used techniques simply focus more on invisibility than how robust they are. Moreover, the trade-off between invisibility and robustness is challenging. To this end, this paper proposes a novel watermark technique that efficiently overcomes the idea of a trade-off between robustness and invisibility, thereby increasing both under most attacks. Schur decomposition and a dynamic weighting factors matrix are added to the embedding process to improve the robustness of the proposed technique. Besides that, the embedding function is improved to simultaneously maximize imperceptibility and robustness. Another key contribution of the proposed approach is its use of a trajectory-based optimization algorithm rather than the more prevalent population-based algorithms to determine the optimal scaling factors. Consequently, the proposed technique rapidly identifies the best scaling factors for the embedding function. Statistical analysis is performed using the Friedman test. Experimental results show that the proposed technique outperforms other existing techniques for different sizes, shapes, and types of watermarks.