A Deep Learning Approach to Detect Pornography Videos in Educational Repositories. Freitas, P. V. A. d., Busson, A. J. G., Guedes, Á. L. V., & Colcher, S. In Anais do Simpósio Brasileiro de Informática na Educação, pages 1253–1262, 2020. Conference Name: Anais do XXXI Simpósio Brasileiro de Informática na Educação Publisher: SBC![A Deep Learning Approach to Detect Pornography Videos in Educational Repositories [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper Year doi abstract bibtex 8 downloads Resumo A large number of videos are uploaded on educational platforms every minute. Those platforms are responsible for any sensitive media uploaded by their users. An automated detection system to identify pornographic content could assist human workers by pre-selecting suspicious videos. In this paper, we propose a multimodal approach to adult content detection. We use two Deep Convolutional Neural Networks to extract high-level features from both image and audio sources of a video. Then, we concatenate those features and evaluate the performance of classifiers on a set of mixed educational and pornographic videos. We achieve an F1-score of 95.67% on the educational and adult videos set and an F1-score of 94% on our test subset for the pornographic class.
@inproceedings{freitas_deep_2020,
title = {A Deep Learning Approach to Detect Pornography Videos in Educational Repositories},
rights = {Copyright (c)},
url = {https://sol.sbc.org.br/index.php/sbie/article/view/12881},
doi = {10.5753/cbie.sbie.2020.1253},
abstract = {Resumo
A large number of videos are uploaded on educational platforms every minute. Those platforms are responsible for any sensitive media uploaded by their users. An automated detection system to identify pornographic content could assist human workers by pre-selecting suspicious videos. In this paper, we propose a multimodal approach to adult content detection. We use two Deep Convolutional Neural Networks to extract high-level features from both image and audio sources of a video. Then, we concatenate those features and evaluate the performance of classifiers on a set of mixed educational and pornographic videos. We achieve an F1-score of 95.67\% on the educational and adult videos set and an F1-score of 94\% on our test subset for the pornographic class.},
pages = {1253--1262},
booktitle = {Anais do Simpósio Brasileiro de Informática na Educação},
author = {Freitas, Pedro V. A. de and Busson, Antonio J. G. and Guedes, Álan L. V. and Colcher, Sérgio},
urlyear = {2021},
year = {2020},
langid = {english},
note = {Conference Name: Anais do {XXXI} Simpósio Brasileiro de Informática na Educação
Publisher: {SBC}},
}
Downloads: 8
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