Optimizing Robustness and Invisibility in Digital Image Watermarking: A SVM-Based Multi-Level DWT and SVD Approach

Abstract

This research introduces a new digital image watermarking approach that utilizes discrete wave transformation (DWT), Support vector machine, and singular value decomposition. The method improves robustness under various assault situations by using the SVM classifier during watermark extraction. Multi-level DWT splits the host picture into sub-bands when embedding, and the coefficients are used as input for SVM. After SVD, the scaling factor embeds the watermark. Comparing the proposed approach to existing research under various attacks, the experimental findings demonstrate that it strikes an equilibrium between robustness and invisibility for watermarks of varying sizes. Support Vector Machine is a contemporary category of machine learning techniques that is extensively employed for the purpose of solving classification problems.