Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform

Abstract

In recent years, digital image watermarking has gained a significant amount of popularity and developed into a crucial and essential tool for copyright protection, security, and the identification of multimedia content. Despite its high computational complexity, singular value decomposition (SVD) is an extensively utilized transformation in digital image watermarking. This research presents a robust and blind image watermarking scheme that directly alters the image pixels in the spatial domain to incorporate the watermark by quantizing the block-wise invariant maximum singular value. Using a distribution rule, pixels from the cover image are redistributed to obtain a new image that is divided into square and non-overlapping blocks to obtain invariant maximum singular values by using the matrix 2-norm in the spatial domain without performing an SVD transform. This modifies the pixels of the cover image such that the outcome is equivalent to the difference between the maximum singular values of the corresponding blocks in covers and watermarked images. The strengths of the proposed approach are highlighted by a comparison of experimental results with the most recent and comparable watermarking approaches.

A Robust Zero-Watermarking Scheme in Spatial Domain by Achieving Features Similar to Frequency Domain

“…This segment provides a concise overview of the principles associated with the proposed watermarking scheme. For additional information, researchers may consult the accompanying references [3,12,13,30].…”

“…Singular value decomposition (SVD) [3] is a powerful tool that is utilized to decompose a rectangular matrix into three matrices: two orthogonal vector matrices and one singular value matrix. There are several applications for it, including data analysis, image processing, and satellite data.…”

“…Therefore, it is necessary to defend digital media copyrights. To address such concerns, digital watermarking technology was developed [1][2][3][4], and it has since evolved into a distinct area of study that is receiving considerable attention from scholars [5][6][7]. By incorporating watermark information into digital content, digital watermarking ensures its copyright protection.…”

In-depth exploration of digital image watermarking with discrete cosine transform and discrete wavelet transform

“…Geometric transformations can distort the watermark or shift its position, while compression algorithms may introduce artifacts that interfere with the watermark. Despite these limitations, spatial domain techniques offer ease of use and efficiency, making them suitable for real-time applications or scenarios with limited computational resources [10]. When selecting spatial domain watermarking techniques, it is important to consider the desired level of visibility, robustness, and the specific requirements of the application or the media being watermarked [11].…”