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Link to original content: https://doi.org/10.1007/s11042-019-08169-w
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Copy-move forgery detection based on adaptive keypoints extraction and matching

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Abstract

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) schemes extract image keypoints and employ local image features to identify duplicated regions, which exhibits remarkable detection performance with respect to memory requirement, computational cost, and robustness. To enhance the performance of keypoint-based CMFD approaches, here are three issues that need to be solved: the non-uniform distribution of image keypoints, the low discriminatory power of local image descriptor, and the high computational cost and low matching efficiency of feature matching strategy. In order to overcome these issues, we propose a new copy-move forgery detection method based on adaptive keypoints extraction and matching in this paper. First, we extract the image keypoints using the adaptive uniform distribution threshold. Second, the binary robust invariant scalable keypoints (BRISK) descriptor is introduced to represent the local image feature of image keypoints. Afterwards, local BRISK descriptors are employed to match image keypoints by using embedded random ferns approach, which formulates the required matching as a discriminative classification problem. Finally, the falsely matched keypoints pairs are eliminated by utilizing the random sample consensus (RANSAC), and the fast mean-residual normalized intensity correlation (NNPROD) is employed to locate the tampering area. We evaluate the performance of the proposed CMFD method in detail by conducting several simulation experiments, and the experimental results have shown that the detection and localization accuracy of the proposed method is superior to that of the state-of-the-art approaches recently proposed in the literature, even in adverse conditions.

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Acknowledgments

This work was supported partially by the National Natural Science Foundation of China (Nos. 61701212 & 61472171), China Postdoctoral Science Foundation (No. 2017 M621135, 2018 T110220), and High-level Innovation Talents Foundation of Dalian (No.2017RQ055).

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

    Hong-Ying Yang, Shu-Ren Qi, Ying Niu, Pan-Pan Niu & Xiang-Yang Wang

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  1. Hong-Ying Yang
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Correspondence to Pan-Pan Niu or Xiang-Yang Wang.

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Yang, HY., Qi, SR., Niu, Y. et al. Copy-move forgery detection based on adaptive keypoints extraction and matching. Multimed Tools Appl 78, 34585–34612 (2019). https://doi.org/10.1007/s11042-019-08169-w

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