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Multimedia Tools and Applications

, Volume 77, Issue 12, pp 15093–15110 | Cite as

An improved block-based matching algorithm of copy-move forgery detection

  • Yuecong Lai
  • Tianqiang Huang
  • Jing Lin
  • Henan Lu
Article
  • 161 Downloads

Abstract

Copy-move forgery is a common way of image tampering. Matching algorithm is the key step in copy-move forgery detection. Usually, the classical block-based matching algorithm (CBMA) can’t find all matched sub-blocks. In this paper, we propose an improved block-based matching algorithm (IBMA) to solve the problem. Firstly, we put the sum of feature vectors in the first column to get a new matrix. Secondly, the matrix is sorted by first column. Finally, every row of the matrix will search the following rows until the difference in the first column is larger than the threshold value. Experiment results show that the improved block-based matching algorithm is better than the classical block-based matching algorithm when an image was distorted by Gaussian noise, salt-pepper noise, or JPEG compression. The reason is that improved block-based matching algorithm can look for all matched sub-blocks, which makes copy-move forgery detection methods more robust.

Keywords

Digital image forensics Copy-move forgery Region duplication detection 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61070062,61502103) ,the Industry-University Cooperation Major Projects in Fujian Province (Grant No. 2015H6007),Fuzhou science and technology project (Grant No. 2014-G-76),the Science and Technology Department of Fujian province K-class Foundation Project(Grant No. JK2011007),and The Education Department of Fujian Province A-class Foundation Project (Grant No. JA10064).

References

  1. 1.
    Al-Qershi OM, Khoo BE (2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231(1):284–295CrossRefGoogle Scholar
  2. 2.
    Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214(1):33–43CrossRefGoogle Scholar
  3. 3.
    CASIA tampered image detection evaluation (TIDE) database, v2.0, http://forensics.idealtest.org (2011)
  4. 4.
    Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensic Secur 7(6):1841–1854CrossRefGoogle Scholar
  5. 5.
    Fridrich J, Soukal D, Lukáarticleš J (2003) Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research WorkshopGoogle Scholar
  6. 6.
    Hu MK (1962) Visual pattern recognition by moment invariants. IEEE Trans Inf Theory 8(2):179–187CrossRefMATHGoogle Scholar
  7. 7.
    Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206(1):178–184CrossRefGoogle Scholar
  8. 8.
    Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1):59–67MathSciNetCrossRefGoogle Scholar
  9. 9.
    Li L, Li S, Zhu H, Wu X (2014) Detecting copy-move forgery under affine transforms for image forensics. Comput Electr Eng 40(6):1951–1962CrossRefGoogle Scholar
  10. 10.
    Liu G, Wang J, Lian S, Wang Z (2011) A passive image authentication scheme for detecting region-duplication forgery with rotation. Jnetw Comput Appl 34 (5):1557–1565CrossRefGoogle Scholar
  11. 11.
    Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy-rotate-move forgery using Zernike moments. In: Information Hiding, pp 51–65Google Scholar
  12. 12.
    Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image R 29:16–32CrossRefGoogle Scholar
  13. 13.
    Uncompressed Colour Image Dataset. http://homepages.lboro.ac.uk/cogs/datasets/ucid/ucid.html (2003)
  14. 14.
    Wang J, Liu G, Li H, Dai Y, Wang Z (2009) Detection of image region duplication forgery using model with circle block. In: International Conference on Multimedia Information Networking and Security, pp 25–29Google Scholar
  15. 15.
    Zhou L, Chao HC, Vasilakos AV (2011) Joint forensics-scheduling strategy for delay-sensitive multimedia applications over heterogeneous networks. IEEE J Sel Area Comm 29(7):1358–1367CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017
Corrected publication September/2017

Authors and Affiliations

  • Yuecong Lai
    • 1
  • Tianqiang Huang
    • 2
    • 3
  • Jing Lin
    • 1
  • Henan Lu
    • 1
  1. 1.School of Mathematics and Computer ScienceFujian Normal UniversityFuzhouChina
  2. 2.Faculty of SoftwareFujian Normal UniversityFuzhouChina
  3. 3.Fujian Engineering Research Center of Public Service Big Data Mining and ApplicationFujian Normal UniversityFuzhouChina

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