Afrig Aminuddin creator author. text my 2024 2024 monographic eng xiII, 130 pages : illustrations ; 30 cm. + 1 CD-ROM This research proposes a novel image watermarking scheme with self-recovery capability to authenticate and recover tampered images using the embedded watermark data. The scheme employs a new texture preservation technique to maintain the details of the image texture in the recovered image. The texture set is adaptively computed according to the image content. Each image has its own texture set tailored to the characteristics of the image. The scheme generates the watermark data from each image block, comprising average, range, and texture values. The scheme embeds the watermark data into 2 LSB of the original cover image using a block map and a secret key. The scheme also employs a texture preservation technique to enhance the quality of the recovered image. The scheme utilizes a three-layer authentication process to detect and localize the tampered regions. The scheme solves the issue of tampering coincidence using a new block mapping technique, a tamper coincidence localization, and an image inpainting technique. The experimental results show that the proposed scheme can achieve highquality watermarked and recovered images and high tamper detection and localization accuracy under various attack scenarios. The texture preservation technique produces a high-quality recovered image with a PSNR value of 40.04 dB and SSIM value of 0.9953 under 10% tampering rates on the USC-SIPI dataset. The scheme also outperforms several existing watermarking schemes with self-recovery regarding the imperceptibility of the watermarked and recovered images. Image authentication is crucial for legal validity, ensuring digital evidence is admissible and credible in court. In journalism, it prevents the spread of misinformation, maintaining the integrity of news visuals. Across various sectors, including healthcare and historical archives, it upholds accuracy and trustworthiness in using and preserving digital images. specialized Afrig Aminuddin Faculty of Computing Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024 Includes bibliographical references Faculty of Computing Dissertations Universities and colleges Dissertations Theses Dissertations THE0010059 (Local) UMPSA 250514 20251125111039.0 eng