Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

Abstract— Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching. A novel copy–move forgery detection scheme using adaptive oversegmentation and feature point matching is proposed in this paper. The proposed scheme integrates both block-based and key point-based forgery detection methods. First, the proposed adaptive < Final Year Projects 2016 > oversegmentation algorithm segments the host image into no overlapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the forgery region extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions. Finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the proposed copy–move forgery detection scheme can achieve much better detection results even under various challenging conditions compared with the existing state-of-the-art copy–move forgery detection methods.