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Copy-move forgery detection based on scaled ORB

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Abstract

To solve the problem of the false matching and low robustness in detecting copy-move forgeries, a new method was proposed in this study. It involves the following steps: first, establish a Gaussian scale space; second, extract the orientated FAST key points and the ORB features in each scale space; thirdly, revert the coordinates of the orientated FAST key points to the original image and match the ORB features between every two different key points using the hamming distance; finally, remove the false matched key points using the RANSAC algorithm and then detect the resulting copy-move regions. The experimental results indicate that the new algorithm is effective for geometric transformation, such as scaling and rotation, and exhibits high robustness even when an image is distorted by Gaussian blur, Gaussian white noise and JPEG recompression; the new algorithm even has great detection on the type of hiding object forgery.

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Acknowledgments

The research is partly supported by National Natural Science Foundation for young (No. 61305046).

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Authors and Affiliations

  1. Jilin University, Changchun, China

    Ye Zhu, Xuanjing Shen & Haipeng Chen

  2. College of Computer Science and Technology, Jilin University, No. 2699, Str. Qianjing, Changchun, 130012, China

    Ye Zhu, Xuanjing Shen & Haipeng Chen

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  1. Ye Zhu
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  2. Xuanjing Shen
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  3. Haipeng Chen
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Correspondence to Haipeng Chen.

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Zhu, Y., Shen, X. & Chen, H. Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75, 3221–3233 (2016). https://doi.org/10.1007/s11042-014-2431-2

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  • DOI: https://doi.org/10.1007/s11042-014-2431-2

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