ENHANCING WATERMARKING TECHNIQUES USING SVD TRANSFORM AND THE GRASSHOPPER OPTIMIZATION ALGORITHM

Watermarking is a contemporary technique widely utilized to improve security and conceal sensitive data. In the realm of image watermarking, a hidden image is embedded within a host image in such a way that it remains imperceptible to unauthorized individuals. However, the hidden image can be extracted when needed, serving as definitive proof of ownership for digital assets. The two primary considerations in image watermarking are achieving a high level of transparency for the host image and ensuring robustness against potential attacks. This study introduces a novel approach combining digital watermarking (DW) and singular value decomposition (SVD) transformations to embed the hidden image within the input image effectively. To optimize the watermark extraction process, the Grasshopper Optimization Algorithm (GOA) is employed to determine the most suitable scaling factor. Additionally, concepts from fractional equations are integrated into the watermarking framework to enhance the system’s robustness and adaptability to complex scenarios. The integration of fractional equations provides a multi-scale perspective that aligns conceptually with fractal-like structures, enabling the watermarking process to better capture the complex, multi-dimensional nature of image data. This approach ensures improved resistance against diverse attack patterns, which often mimic natural irregularities observed in fractal patterns. The experimental results illustrate that the proposed algorithm achieves significant improvements in transparency and robustness compared to existing benchmark methods, highlighting its effectiveness in modern DW applications.