Image forgery detection using adaptive over segmentation and feature point matching
Abstract—Abstract A novel copy-move forgery detection scheme using adaptive over-segmentation and feature point matching is proposed in this paper. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. First, the proposed Adaptive Over-Segmentation algorithm segments the host image into non-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 copymove forgery detection methods.Index Terms: Copy-Move Forgery Detection, Adaptive Over-Segmentation, Local Color Feature, Forgery Region Extraction.
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