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Multiple Copy-Move Forgery Detection Based on Density Clustering

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Abstract

We propose a new copy-move forgery detection method, which can solve the problems of multiple copy-move forgery, low accuracy and inaccurate tampered region location. First, keypoints and corresponding features of the image are extracted by using AKAZE (accelerated KAZE). Second, features are matched by using the Hamming distance and g2NN which can detect the multiple copy-move forgery. Then, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to cluster the keypoints and remove false matching. Finally, PSNR (peak signal-to-noise ratio) and morphological processing is used to locate tampered region accurately. Experimental results show that the proposed method performs well on geometric transformation, post-processing, multiple copy-move forgery, and tampered region localization.

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Funding

The study was supported by the Research Fund Project of North China Institute of Aerospace Engineering (KY-2018-30).

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Authors and Affiliations

  1. College of Computer, North China Institute of Aerospace Engineering, 065000, Langfang, China

    X. H. Zhou

  2. Zhonggong Huatong (Beijing) Technology Development Co., Ltd., 100089, Beijing, China

    Q. J. Shi

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  1. X. H. Zhou
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  2. Q. J. Shi
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Correspondence to X. H. Zhou or Q. J. Shi.

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Conflict of interest. The authors declare that they have no conflicts of interest.

Ethical approval. This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Xuehua Zhou. Born in 1989. Received her master’s degree from Jilin University in 2018 with specialty in Computer Technology. Currently work as assistant at the college of computer, North China Institute of Aerospace Engineering. Her research interests include image processing and artificial intelligence, in particular, copy-move forgery detection. Author of 1 publication.

Qingjie Shi. Born in 1990. Received his master’s degree from Beijing Jiaotong University in 2016 with specialty in Electronic and Communication Engineering. Currently work as Engineer at Zhonggong Huatong (Beijing) Technology Development Co., Ltd. His research interests include image processing, artificial intelligence, and smart highway.

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Zhou, X.H., Shi, Q.J. Multiple Copy-Move Forgery Detection Based on Density Clustering. Pattern Recognit. Image Anal. 31, 109–116 (2021). https://doi.org/10.1134/S1054661821010181

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