  Manipulating video content is easier than ever. Due to the misuse potential
of manipulated content, multiple detection techniques that analyze the pixel
data from the videos have been proposed. However, clever manipulators should
also carefully forge the metadata and auxiliary header information, which is
harder to do for videos than images. In this paper, we propose to identify
forged videos by analyzing their multimedia stream descriptors with simple
binary classifiers, completely avoiding the pixel space. Using well-known
datasets, our results show that this scalable approach can achieve a high
manipulation detection score if the manipulators have not done a careful data
sanitization of the multimedia stream descriptors.
