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Robust copy-move forgery detection method using pyramid model and Zernike moments

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Abstract

Copy-move forgery detection (CMFD) is probably one of the most active research subtopics within the blind image forensics field. Among existing algorithms, block-based methods achieve better detection result, but suffer from not being robust to the scaling of the copied region, which is limited to a small scaling range between 91 and 109%. To overcome this issue, a novel copy-move forgery detection method based on the pyramid model and Zernike moments is proposed in this work. First, a non-uniform sampling approach is used to build the pyramid model to improve the speed of detection, where each level of the model is a rescaled image with different scale factors. Then, the Zernike moments method is used to extract image rotation invariant feature. Furthermore, to make the proposed model be robust to the rotation and the combination of the rotation and scaling of the copied region, the random sample consensus (RANSAC) algorithm is adopted to look for the same affine transformation parameters. Compared with the existing methods, experimental results show that the proposed method has a good effect on the arbitrary rotation angle, the scaling range between 50 and 200%, and the combination of both.

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Acknowledgements

The authors would like to thank all the anonymous reviewers for their helpful comments and suggestions. This work was supported by the natural science foundation of Hunan province under Grants 2017JJ2099, 2017JJ3091, by the Hunan province education department under Grants 16C0642, 17C0643, by the Doctor Fund University of Science and Technology of Hunan under Grants E51684, E51754, by the National Natural Science Foundation of China 61702179, by National Science and Technology Support Project of China, under grant number 2015BAF32B01.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Junlin Ouyang, Yizhi Liu & Miao Liao

  2. Key Laboratory of Knowledge Processing and Networked Manufacturing, College of Hunan Province, Hunan University of Science and Technology, Xiangtan, 411201, China

    Junlin Ouyang & Yizhi Liu

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  1. Junlin Ouyang
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  2. Yizhi Liu
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Correspondence to Junlin Ouyang.

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Ouyang, J., Liu, Y. & Liao, M. Robust copy-move forgery detection method using pyramid model and Zernike moments. Multimed Tools Appl 78, 10207–10225 (2019). https://doi.org/10.1007/s11042-018-6605-1

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