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Region duplication detection based on hybrid feature and evaluative clustering

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Abstract

Digital image are easy to be tampered by the photo editing software. Therefore, digital image forensics which aims at validating the authenticity of the digital image are received wide public concern. Region duplication is a commonly used operation in digital image forgeries. The main aims of the region duplication are to overemphasize or conceal some contents by duplicating some regions on the image. Most of the region duplication methods can be categorized into two main classes:block-based and keypoint-based methods. In this paper, a novel region duplication detection scheme is proposed based on hybrid feature and evaluative clustering. The proposed scheme is divided into two stages: the rough matching and the exact matching. In the rough matching, first, hybrid keypoints are extracted from the input image, and those keypoints are described by the unified descriptors. Second, those keypoints are matched by the g2NN strategy. Third, those matched keypoints are grouped by the proposed clustering based on evaluation. Fourth, affine transformations are estimated between these groups, and Bag of Word is used to filter inaccuracy affine transformations to improve the results of pixel level. When no affine transformation is obtained, in the exact matching, each suspicious region is handled separately. Experimental results indicate that the proposed scheme outperforms the state-of-the-art methods under various conditions.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. U1736118), the National Key R&D Program of China (No. 2017YFB0802500), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45), the Science and Technology Planning Project of Guangdong Province (No.2017A040405051), the Alibaba Group through Alibaba Innovative Research Program.

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  1. School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Key Laboratory of Machine Intelligence and Advanced Computing (Ministry of Education), Sun Yat-sen University, Guangzhou, 510006, China

    Cong Lin, Wei Lu & Xinchao Huang

  2. Center for Faculty Development and Educational Technology, Guangdong University of Finance and Economics, Guangzhou, 510320, China

    Cong Lin & Hanhui Lin

  3. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Wei Lu

  4. School of Electronics and Information Technology, Key Laboratory of Information Technology (Ministry of Education), Sun Yat-sen University, Guangzhou, 510006, China

    Ke Liu & Wei Sun

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  1. Cong Lin
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Lin, C., Lu, W., Huang, X. et al. Region duplication detection based on hybrid feature and evaluative clustering. Multimed Tools Appl 78, 20739–20763 (2019). https://doi.org/10.1007/s11042-019-7342-9

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