Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342271
Type: Artigo
Title: Combating the Elsagate phenomenon: deep learning architectures for disturbing cartoons
Author: Ishikawa, A.
Bollis, E.
Avila, S.
Abstract: Watching cartoons can be useful for children's intellectual, social and emotional development. However, the most popular video sharing platform today provides many videos with Elsagate content. Elsagate is a phenomenon that depicts childhood characters in disturbing circumstances (e.g., gore, toilet humor, drinking urine, stealing). Even with this threat easily available for children, there is no work in the literature addressing the problem. As the first to explore disturbing content in cartoons, we proceed from the most recent pornography detection literature applying deep convolutional neural networks combined with static and motion information of the video. Our solution is compatible with mobile platforms and achieved 92.6% of accuracy. Our goal is not only to introduce the first solution but also to bring up the discussion around Elsagate.
Subject: Redes neurais convolucionais
Desenho animado
Editor: Institute of Electrical and Electronics Engineers Inc.
Rights: Fechado
Identifier DOI: 10.1109/IWBF.2019.8739202
Address: https://ieeexplore.ieee.org/document/8739202
Date Issue: 2019
Appears in Collections:IC - Artigos e Outros Documentos

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