Digital video forgery detection based on statistical features calculation

Andrey Kuznetsov

doi:10.1117/12.2559499

31 January 2020 Digital video forgery detection based on statistical features calculation

Andrey Kuznetsov

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114332O (2020) https://doi.org/10.1117/12.2559499
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Fake digital information is distributed heavily nowadays using social networks, new and other information sources. Digital forgeries use may lead to an unexpected result and it is quite difficult to detect tampering with just an expert view. A lot of algorithms for digital image forgery detection exist, but video forgery detection is on its early development stage. We propose a new approach for digital video forgery detection, which is based on statistical features calculation on difference shift frames. We selected three types of features for research: CC-PEV, SPAM and MP-486. We also estimated the quality of several classification techniques to detect altered frames: RBF-based SVM, linear ensemble classifier and decision tree. The experimental results showed the best combination of feature and classification algorithms for the video forgery detection problem solution.

Citation Download Citation

Andrey Kuznetsov "Digital video forgery detection based on statistical features calculation", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114332O (31 January 2020); https://doi.org/10.1117/12.2559499

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