Multimedia Tools and Applications

, Volume 75, Issue 2, pp 1159–1176 | Cite as

Feature point-based copy-move forgery detection: covering the non-textured areas



Detection of copy-move forgery has recently attracted much attention. During the past decade, two categories of methods, namely block-based and feature point-based methods, gradually developed. Compared with block-based methods, feature point-based methods exhibit remarkable performance with respect to robustness and computational cost. However, the feature point-based methods are still incomplete especially in terms of forgeries involving small smooth regions. In this paper, we solve this problem by cautiously supplementing redundant feature points and feature fusion. We propose two-stage feature detection to obtain better feature coverage and enhance the matching performance by combining the MROGH and HH descriptor. We evaluated our method on two representative datasets. We use precision, recall and F 1 score to quantitatively evaluate the performance. Experimental results confirm the efficacy of our work.


Copy-Move Detection MROGH Descriptor Hue Histogram Feature matching Feature fusion 



The authors would like to thank Yuenan Li and Irene Amerini for sharing their copy-move forgery detection code. We also thank Mahmoud Emam and all the anonymous reviewers for their helpful comments and suggestions. Additionally, this work is supported by the National Natural Science Foundation of China (61100187 and 61361166006) and the China Postdoctoral Science Foundation (2011M500666).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of Computer Science and TechnologyMudanjiang Normal UniversityMudanjiangPeople’s Republic of China

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