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A new keypoint-based copy-move forgery detection for small smooth regions

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Abstract

Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61472171 & 61272416, the Natural Science Foundation of Liaoning Province of China under Grant No. 201602463, A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology.

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China

    Xiang-Yang Wang, Shuo Li, Yu-Nan Liu, Ying Niu & Hong-Ying Yang

  2. Jiangsu Engineering Center of Network Monitoring & School of Computer and Software, Nanjing University of Information Science & Technology (NUIST), Nanjing, 210044, China

    Zhi-li Zhou

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  1. Xiang-Yang Wang
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  2. Shuo Li
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  4. Ying Niu
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  5. Hong-Ying Yang
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Correspondence to Xiang-Yang Wang or Hong-Ying Yang.

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Wang, XY., Li, S., Liu, YN. et al. A new keypoint-based copy-move forgery detection for small smooth regions. Multimed Tools Appl 76, 23353–23382 (2017). https://doi.org/10.1007/s11042-016-4140-5

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  • DOI: https://doi.org/10.1007/s11042-016-4140-5

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