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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Kok-Why Ng
Multimedia University
Electrical & Electronics Engineering

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Blending of three-dimensional geometric model shapes Seng-Beng Ng; Kok-Why Ng; Rahmita Wirza O.K. Rahmat; Yih-Jian Yoong
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp102-109

Abstract

Three-dimensional (3D) geometric model shapes blending method can create various in-between models from two inputs of models shapes. Though, many blended shapes are implausible due to different inputs of model type, inappropriate matching-parts, improper parts-segmentation, and non-tally number of segmentation parts. are crucial and should be taken into account. The objective of this paper is to study the strengths and weaknesses of some prominent shapes blending methods and the 3D reconstruction methods. An interpolated shape blending program using the Laplacian-based contraction and Slinky-based segmentation method is developed to illustrate the critical problems arise in the shape blending process. Output results are to be compared with some prominent existing methods and one will observe the potential research direction in the blending research work
Image copy-move forgery detection: a survey of methods, datasets, and emerging trends Li-xian Jiao; Kok-Why Ng; Hau-Lee Tong
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10796

Abstract

Digital image forgery has become a critical concern in the era of advanced multimedia technologies, where the authenticity of visual content directly affects trust in digital communication, journalism, and law enforcement. Among various forgery techniques, copy-move forgery (CMF) is among the most common and deceptive, as it involves duplicating a region of an image to conceal or misrepresent information. To address this challenge, numerous copy-move forgery detection (CMFD) approaches have been proposed, ranging from block-based and keypoint-based methods to hybrid models and deep learning (DL) techniques. This paper provides a comprehensive review of these approaches, analyzing their strengths and limitations, and evaluating their performance across multiple benchmark datasets. The evaluation considers factors such as image resolution, manipulation types, and robustness against post-processing attacks. By systematically comparing the algorithms and datasets, the study highlights persistent challenges and outlines future research directions. The findings aim to guide researchers in selecting appropriate techniques and inspire the development of more robust CMFD solutions.
Co-Authors Hau-Lee Tong Li-xian Jiao Rahmita Wirza O.K. Rahmat Seng-Beng Ng Yih-Jian Yoong