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.
@article{arxiv.1906.08743,
title = {We Need No Pixels: Video Manipulation Detection Using Stream Descriptors},
author = {David Güera and Sriram Baireddy and Paolo Bestagini and Stefano Tubaro and Edward J. Delp},
journal= {arXiv preprint arXiv:1906.08743},
year = {2019}
}
Comments
7 pages, 6 figures, presented at the ICML 2019 Worksop on Synthetic Realities: Deep Learning for Detecting AudioVisual Fakes