  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.
