YouTube is the leading social media platform for sharing videos. As a result, it is plagued with misleading content that includes staged videos presented as real footages from an incident, videos with misrepresented context and videos where audio/video content is morphed. We tackle the problem of detecting such misleading videos as a supervised classification task. We develop UCNet - a deep network to detect fake videos and perform our experiments on two datasets - VAVD created by us and publicly available FVC [8]. We achieve a macro averaged F-score of 0.82 while training and testing on a 70:30 split of FVC, while the baseline model scores 0.36. We find that the proposed model generalizes well when trained on one dataset and tested on the other.
@article{arxiv.1901.08759,
title = {Misleading Metadata Detection on YouTube},
author = {Priyank Palod and Ayush Patwari and Sudhanshu Bahety and Saurabh Bagchi and Pawan Goyal},
journal= {arXiv preprint arXiv:1901.08759},
year = {2019}
}
Comments
Accepted at European Conference on Information Retrieval(ECIR) 2019. 7 Pages