Using Efficient Linear Local Features in the Copy-Move Forgery Detection Task
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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.
KeywordsCopy-move Forgery Duplicate Hash function Hash table Galois field Linear local features
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.
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