We Need No Pixels: Video Manipulation Detection Using Stream Descriptors
This addresses the challenge of video forgery detection for security applications, but it is incremental as it builds on existing detection techniques by focusing on metadata.
The paper tackles the problem of detecting manipulated videos by analyzing multimedia stream descriptors instead of pixel data, achieving high detection scores when manipulators do not sanitize these descriptors.
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.