Abstract
Digital images are often used to prove some facts or events, but nobody can guarantee their originality. More often we can see in TV news, that some satellite imagery evidences were received to show what has happened. However, we cannot be sure, that these data were not changed by some hackers. In this paper we propose a new algorithm for detection of the most frequently used attack plain copy-move. The algorithm is based on a hash value calculation in a sliding window mode. The hash function is constructed using efficient linear local features that were developed by coauthor V. Myasnikov in 2010. Finally, we present results of conducted experiments and comparison with existing solutions, as well as recommendations for the use of the proposed approach. The main advantage of the proposed solution is 99.95% precision of copy-move blocks detection comparing with existing approaches. Another impact is that it can be easily used for large satellite image analysis as well as ordinary digital images processing because of low computational complexity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farid, H.: Image forgery detection. IEEE Sig. Process. Mag. 26, 16–25 (2009)
Gong, J., Guo, J.: Exposing region duplication through local geometrical color invariant features. J. Electron. Imaging 24(3), 033010 (2015)
Bayram, S., Sankur, B., Memon, N.: Image manipulation detection. J. Electron. Imaging 15(4), 041102 (2006)
Cao, Y., Gao, T., Fan, L., Yang, Q.: A robust detection algorithm for copy-move forgery in digital images. Forensic Sci. Int. 214, 33–43 (2012)
Vladimirovich, K.A., Valerievich, M.V.: A fast plain copy-move detection algorithm based on structural pattern and 2D Rabin-Karp rolling hash. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8814, pp. 461–468. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11758-4_50
Glumov, N., Kuznetsov, A., Myasnikov, V.: The algorithm for copy-move detection on digital images. Comput. Opt. 37(3), 361–368 (2013)
Myasnikov, V.: Efficient mutually-calculated features for linear local description of signals and images. In: IASTED International Conference on Automation, Control, and Information Technology - Information and Communication Technology (ACIT-2010), pp. 29–34 (2010)
Myasnikov, V.: Constructing efficient linear local features in image processing and analysis problems. Autom. Remote Contr. 72, 514–527 (2010)
Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)
Acknowledgements
The proposed copy-move detection algorithm and the hash function based on efficient linear local features (Sects. 2, 3 and 4) were developed with support from the Russian Science Foundation grant №14-31-00014 “Establishment of a Laboratory of Advanced Technology for Earth Remote Sensing”. The experimental results (Sect. 5) were obtained with support from the Russian Foundation for Basic Research grant №16-37-00056.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kuznetsov, A., Myasnikov, V. (2017). Using Efficient Linear Local Features in the Copy-Move Forgery Detection Task. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-52920-2_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52919-6
Online ISBN: 978-3-319-52920-2
eBook Packages: Computer ScienceComputer Science (R0)