LYU Yingda, SHEN Xuanjing, CHEN Haipeng. Copy-Paste Detection Based on a SIFT Marked Graph Feature Vector[J]. Chinese Journal of Electronics, 2017, 26(2): 345-350. doi: 10.1049/cje.2017.01.028
Citation: LYU Yingda, SHEN Xuanjing, CHEN Haipeng. Copy-Paste Detection Based on a SIFT Marked Graph Feature Vector[J]. Chinese Journal of Electronics, 2017, 26(2): 345-350. doi: 10.1049/cje.2017.01.028

Copy-Paste Detection Based on a SIFT Marked Graph Feature Vector

doi: 10.1049/cje.2017.01.028
Funds:  This work is supported by the National Natural Science Foundation of China (No.61305046), and the Natural Science Foundation of the Jilin Province (No.20140101193JC, No.20130522117JH, No.20150101055JC).
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  • Corresponding author: CHEN Haipeng (corresponding author) was born in 1978, is a associate professor of the college of computer science and technology, Jilin University. His research interests include image processing,pattern recognition, identification for image authenticity, pattern recognition, and computer network security. (Email:chenhp@jlu.edu.cn)
  • Received Date: 2014-11-04
  • Rev Recd Date: 2015-01-20
  • Publish Date: 2017-03-10
  • To detect copy-paste tampering, an improved SIFT (Scale invariant feature transform)-based algorithm was proposed. Maximum angle is defined and a maximum angle-based marked graph is constructed. The marked graph feature vector is provided to each SIFT key point via discrete polar coordinate transformation. Key points are matched to detect the copy-paste tampering regions. The experimental results show that the proposed algorithm can effectively identify and detect the rotated or scaled copy-paste regions, and in comparison with the methods reported previously, it is resistant to post-processing, such as blurring, Gaussian white noise and JPEG recompression. The proposed algorithm performs better than the existing algorithm to dealing with scaling transformation.
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