SOTAVerified

Misleading Metadata Detection on YouTube

2019-01-25Code Available0· sign in to hype

Priyank Palod, Ayush Patwari, Sudhanshu Bahety, Saurabh Bagchi, Pawan Goyal

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

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.

Tasks

Reproductions