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- W2913749852 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." @default.
- W2913749852 created "2019-02-21" @default.
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- W2913749852 date "2019-01-01" @default.
- W2913749852 modified "2023-09-25" @default.
- W2913749852 title "Misleading Metadata Detection on YouTube" @default.
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- W2913749852 doi "https://doi.org/10.1007/978-3-030-15719-7_18" @default.
- W2913749852 hasPublicationYear "2019" @default.
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