Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

Chi Man Pun, Xiao Chen Yuan, Xiu Li Bi

Research output: Contribution to journalArticlepeer-review

253 Citations (Scopus)

Abstract

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 keypoint-based forgery detection methods. First, the proposed adaptive oversegmentation algorithm segments the host image into nonoverlapping 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.

Original languageEnglish
Article number7086315
Pages (from-to)1705-1716
Number of pages12
JournalIEEE Transactions on Information Forensics and Security
Volume10
Issue number8
DOIs
Publication statusPublished - 1 Aug 2015
Externally publishedYes

Keywords

  • Adaptive Over-Segmentation
  • Copy-Move Forgery Detection
  • Forgery Region Extraction
  • Local Color Feature

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