Multimedia Tools and Applications

, Volume 78, Issue 2, pp 2311–2344 | Cite as

Copy-move forgery detection based on compact color content descriptor and Delaunay triangle matching

  • Xiang-yang WangEmail author
  • Li-xian Jiao
  • Xue-bing Wang
  • Hong-ying YangEmail author
  • Pan-pan Niu


Copy-move (region duplication) is one of the most common types of image forgeries, in which at least one part of an image is copied and pasted onto another area of the same image. The main aims of the copy-move forgery are to overemphasize a concept or conceal objects by duplicating some regions. Keypoint based copy-move forgery detection (CMFD) method extracts image feature points and employs local image features to identify duplicated regions, which exhibits remarkable detection performance with respect to memory requirement, computational cost, and robustness. However, they usually do not work well when the objects are hidden in smooth background areas. Also, the detection and localization accuracy always be lowered because of poor local image feature computation. In this paper, we present a novel approach for the detection and localization of copy-move forgeries, which is based on color invariance SIFER (Scale-invariant feature detector with error resilience) and FQRHFMs (Fast quaternion radial harmonic Fourier moments). Firstly, the original forgery image is segmented into nonoverlapping and nearly uniform superpixel blocks, and the stable keypoints are extracted adaptively from each superpixel block by incorporating the superpixel contents and color invariance SIFER. Secondly, a set of connected Delaunay triangles is constructed using the extracted image keypoints, and suitable local image feature for each Delaunay triangle is computed by using FQRHFMs and gradient entropy. Thirdly, the local image features and coherency sensitive hashing (CSH) are utilized to match quickly the Delaunay triangles. Finally, the falsely matched Delaunay triangles are removed by employing dense linear fitting (DLF), and the duplicated regions are localized using optimized zero mean normalized cross-correlation (ZNCC) measure. We conduct extensive experiments to evaluate the performance of the proposed copy-move forgery detection scheme, in which encouraging results validate the effectiveness of the proposed technique.


Copy-move forgery detection Color invariance SIFER FQRHFMs Coherency sensitive hashing Optimized ZNCC 



This work was supported by the National Natural Science Foundation of China under Grant No. 61472171, 61272416, &61701212, Project Funded by China Postdoctoral Science Foundation No. 2017 M621135, and the Natural Science Foundation of Liaoning Province of China under Grant No. 201602463.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianPeople’s Republic of China

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