Abstract
Great attention is paid to detecting video forgeries nowadays, especially with the widespread sharing of videos over social media and websites. Many video editing software programs are available and perform well in tampering with video contents or even creating fake videos. Forgery affects video integrity and authenticity and has serious implications. For example, digital videos for security and surveillance purposes are used as evidence in courts. In this paper, a newly developed passive video forgery scheme is introduced and discussed. The developed scheme is based on representing highly correlated video data with a low computational complexity third-order tensor tube-fiber mode. An arbitrary number of core tensors is selected to detect and locate two serious types of forgeries which are: insertion and deletion. These tensor data are orthogonally transformed to achieve more data reductions and to provide good features to trace forgery along the whole video. Experimental results and comparisons show the superiority of the proposed scheme with a precision value of up to 99% in detecting and locating both types of attacks for static as well as dynamic videos, quick-moving foreground items (single or multiple), zooming in and zooming out datasets which are rarely tested by previous works. Moreover, the proposed scheme offers a reduction in time and a linear computational complexity. Based on the used computer’s configurations, an average time of 35 s. is needed to detect and locate 40 forged frames out of 300 frames.
Highlights
Recording videos using digital cameras, smartphones, and surveillance camcorders has become very easy and has been performed for many reasons in our everyday activities
The offered approach in this paper develops a new inter-frame forgery passive approach that has high efficiency in respect to the achieved detection accuracy at minimum computational complexity
Instead of spatially comparing the whole pixel correlation through all successive frames, a group of tracing orthogonal features [31,32] is extracted from a third-order tensor representation of tube fiber geometrical frame construction and compared with its successive groups
Summary
Recording videos using digital cameras, smartphones, and surveillance camcorders has become very easy and has been performed for many reasons in our everyday activities. Any editing video software can be used to tamper with videos such as Adobe Video Editor, Photoshop, Premiere by Adobe, and Windows Movie Maker, which are really good methods to edit video content, as anyone can edit the video files as it will be similar to the original content. These software applications have made forgery identification very difficult and have led to serious issues. Detecting forged videos has gained great interest and has become a trending research topic compared to video authentication but authenticating the video contents may be unavailable all the time [1,2]
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