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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31387–31413 | Cite as

Copy move forgery detection based on keypoint and patch match

  • Ke Liu
  • Wei LuEmail author
  • Cong Lin
  • Xinchao Huang
  • Xianjin Liu
  • Yuileong Yeung
  • Yingjie Xue
Article

Abstract

Copy move has become a simple and effective operation for image forgeries due to the advancement of image editing software, which is still challenging to be detected. In this paper, a novel method is proposed for copy move forgery detection based on Keypoint and Patch Match. Local Intensity Order Pattern (LIOP), a robust keypoint descriptor, is combined with SIFT to obtain reliable keypoints. After using g2NN to match the extracted keypoints, a new matched keypoint pair description model and a density grid-based filtering strategy are applied to removing the redundancy matched keypoint pairs. Finally an enhanced patch match approach is utilized to examine the matched keypoint pairs to accurately determine the existence of forgery. Compared with the state-of-the-art methods, the proposed method can detect copy move region more precisely according to the experimental result, even when detected objects are distorted by some processing such as rotation, scaling, JPEG compression and additional noise.

Keywords

Copy move forgery detection Duplicated region localization LIOP SIFT Patch match 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. U1736118), the Key Areas R&D Program of Guangdong (No. 2019B010136002), the Key Scientific Research Program of Guangzhou (No. 201804020068), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), Shanghai Minsheng Science and Technology Support Program (17DZ1205500), Shanghai Sailing Program (17YF1420000), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronics and Information Technology, Guangdong Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhouChina
  2. 2.School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhouChina
  3. 3.Center for Faculty Development and Educational TechnologyGuangdong University of Finance and EconomicsGuangzhouChina

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