Siti Nur Avivah creator author. text my 2025 2025 monographic eng xii, 78 pages : illustrations ; 30 cm. + 1 CD-ROM Digital images are one of the most widely utilized media due to their capacity to transmit information visually. The rapid advancement of network technologies and social media over recent decades has facilitated the widespread distribution of images via the internet. However, this ease of distribution allows unauthorized users to copy and modify the image, potentially infringing on copyright. Digital image watermarking is a robust method to combat copyright violations of digital content. Watermarking can be performed using either single or multiple watermarks. This study proposes multiple watermarking schemes based on Non-Subsampled Shearlet Transform (NSST) and Lifting Wavelet Transform (LWT) using a small block of 4×4. The watermark is embedded specifically in the red and green channels of the host images. NSST-LWT transforms the image channels. Then, the entropy and edge entropy of the LL frequency is calculated to determine the suitable blocks for embedding the watermark based on Human Visual System (HVS). The selected blocks are then decomposed by using DCT. The proposed scheme identifies the optimal embedding locations within the 4×4 DCT coefficient matrix. The selected coefficients at (1,1), (1,2), (2,1), and (2,2) are then transformed using SVD. The singular value in a position of (1,1) is then used to incorporate the watermark bits with a certain rule. The selected DCT coefficients influence the quality and strength of the watermarked image. The experimental results reveal that the suggested scheme has strong imperceptibility and resilience under various manipulation attacks, with average PSNR and SSIM values of 46.88 dB and 0.9981, respectively. Additionally, the robustness performance regarding correlation (NC) values is close to 1. specialized Siti Nur Avivah Faculty of Computing Thesis (Master of Science) -- Universiti Malaysia Pahang – 2025 Includes bibliographical references Faculty of Computing Dissertations Universities and colleges Dissertations Theses Dissertations THE0010074 (Local) UMPSA 250515 20251125111041.0 eng