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The video tampering dataset (VTD) comprises a total of 33 videos, divided among three categories in video tampering: (1) copymove, (2) splicing, and (3) swapping-frames.
Video tampering detection research requires an inclusive database of video modification.
Recently, the video tampering dataset (VTD) was provided by Al-Sanjary et al. [17].
This database has been made publically available for research on splicing, Swapping frames, and copymove tampering, and, as such, various video tampering detection issues with ground truth.
This paper aims to discuss a comprehensive proposal to create a dataset composed of modified videos for forensic investigation, in order to standardize existing techniques for detecting video tampering.
The VTD, focused on video tampering detection on videos collected from the YouTube platform, is composed by 33 downloaded videos, 16-s long, at 30 fps with a HD resolution.
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The original dataset is subdivided into four subsets: one containing unaltered videos; one with videos created by splicing; one with videos manipulated by copy-move; and one with videos tampered by swapping frames.
All video channels have an address that you assign Such as (Outside Front Yard) and is time and date stamped and video is digitally watermarked to prevent video and/or evidence tampering.
Digital forensic experts examined all photographs and video for evidence of tampering and found none, Mr. Macrae said.
Although they use a social media platform to acquire videos and provide interesting tampering techniques, there are not useful information related to the camera or device used.
A good sign that a video's been tampered with is if it's promoting some product or other but, for the life of us, we can't figure out who'd want to market their wares with a baby-stealing golden eagle as a mascot.
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