Year: 2025
Type: article
Abstract: Digital watermarking is crucial in content identification and copyright protection, particularly multimedia and medical imaging. This paper introduces two novel hybrid watermarking methods, Entropy-Guided Singular Embedding (EGSE) and Entropy-Guided Hybrid Embedding (EGHE), that improve upon existing techniques by integrating entropy-based adaptive block selection with Particle Swarm Optimization (PSO) for dynamic embedding strength determination. Unlike traditional methods, which rely on fixed embedding regions or manual parameter tuning, the proposed approaches automatically identify high-entropy regions to embed watermark signals, ensuring stronger resistance to distortion while maintaining image quality. EGSE employs Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD), whereas EGHE enhances robustness by combining IWT, SVD, and the Discrete Cosine Transform (DCT) into a unified embedding domain. Experimental results demonstrate that EGSE achieves excellent imperceptibility with a PSNR of 52.632 dB on general grayscale images. Both methods show high robustness against common attacks such as noise, filtering, and rotation, with EGHE performing particularly well under geometric distortions. The combination of entropy guided embedding approach and PSO optimization presents a lightweight yet highly effective watermarking strategy, making the proposed methods especially suitable for telemedicine and secure multimedia authentication.
Authors: Ooi Jessie, Liew Siau Chuin, Syifak Izhar Bt Hisham, Khor Hui Liang, Khoo Bee Ee, Jasni Mohamad Zain
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Award: G7653821823
OpenAlex ID: W4413398474