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Multimedia Tools and Applications

, Volume 77, Issue 11, pp 14241–14258 | Cite as

Region duplication detection based on image segmentation and keypoint contexts

  • Cong Lin
  • Wei Lu
  • Wei Sun
  • Jinhua Zeng
  • Tianhua Xu
  • Jian-Huang Lai
Article
  • 206 Downloads

Abstract

In this paper, a novel region duplication detection method is proposed based on image segmentation and keypoint contexts. The proposed method includes the primary region duplication detection based on keypoints and the supplementary region duplication detection based on blocks. In the primary region duplication detection, an image is divided into non-overlapped patches by using SLIC. Furthermore, the keypoints are matched and clustered within the same patch as patch feature. Then the patches are matched and an affine transformation is tried to be estimated from a pair of patches. When the estimation fails, in the supplementary region duplication detection, a transformation matrix is tried to be estimated from a pair of keypoints by the proposed Keypoint Contexts (KC) approach. The experimental results indicate that the proposed method can achieve much better comprehensive performances than the state-of-the-art methods on the public databases, even under various challenging conditions.

Keywords

Digital image forensics Region duplication detection Copy-move forgery Image segmentation Keypoint contexts 

Notes

Acknowledgments

This work is supported by the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Fundamental Research Funds for the Central Universities (No. 16lgjc83), Scientific and Technological Achievements Transformation Plan of Sun Yat-sen University, the Research Project of Guangdong University of Finance and Economics (No. 10GL52001), Shanghai Sailing Program (No. 17YF1420000), the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University (Contract No. RCS2016K007), and the Science and Technology Development Fund of Macao SAR (No. 097/2013/A3).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Data and Computer Science, Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.Educational Technology CenterGuangdong University of Finance and EconomicsGuangzhouChina
  3. 3.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  4. 4.School of Electronics and Information Technology, Key Laboratory of Information Technology (Ministry of Education)Sun Yat-sen UniversityGuangzhouChina
  5. 5.State Key Laboratory of Rail Traffic Control and SafetyBeijing Jiaotong UniversityBeijingChina
  6. 6.Institute of Forensic ScienceMinistry of JusticeShanghaiChina

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