IMAGE FORGERY DETECTION USING ADAPTIVE OVER-SEGMENTATION AND FEATURE POINT MATCHING USING MATLAB

Abstract

Digital image forgery has gotten easier to do as computer technology and image processing tools have advanced. The validity of digital images, however, is a significant problem because they are a common source of information. The adaptive over-segmentation and feature point matching approach outlined below is a recommended technique for identifying fake images using a Matlab software method. Block- based and keypoint-based forgery detection techniques are both integrated into the suggested scheme. First, the proposed Adaptive Over-Segmentation algorithm adaptively and non-overlappingly segments the host picture into irregular blocks using image processing methods. Once the feature points have been extracted from each block as block features, the block features are compared to one another to locate the labeled feature points. This process can roughly identify the regions where a forgery is believed to have taken place. To produce the identified forgery regions, it then applies the morphological operation to the merged regions. The experimental findings show that, in comparison to the current state-of-the-art copy-move forgery detection methods, the proposed copy-move forgery detection scheme can produce significantly better detection results even under a variety of difficult conditions. KEYWORDS: Matlab, Adaptive over-segmentation, Colour block feature, Image matching