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Video forgery detection based on nonnegative tensor factorization

Authors: Liguo Yin; Zhengyao Bai; Renqing Yang;

Video forgery detection based on nonnegative tensor factorization

Abstract

With the development and popularity of powerful video editing tools, it tends to be increasingly easier to create digital synthesized videos. A novel passive video inter-frame forgery detection method based on nonnegative tensor factorization (NTF) is presented in this work. It is based on the finding that inter-frame forgery will disturb the consistency of time-dimension factor. In this method, the video is factorized by using NTF first and then the time-dimension factor is extracted. By comparing the correlation between the elements of the factor, the video forgery can be detected. According to the experimental results, it shows that the proposed scheme is able to expose frame deletion and insertion forgery effectively.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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