Combating the Elsagate Phenomenon: Deep Learning Architectures for Disturbing Cartoons

Autor(es)

Ishikawa, Akari ; Bollis, Edson ; Avila, Sandra ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 7TH INTERNATIONAL WORKSHOP ON BIOMETRICS AND FORENSICS (IWBF); v. N/A, p. 6-pg., 2019-01-01.

Resumo

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. (AU)

• Processo FAPESP: 17/16246-0 - Análise de mídias sensíveis usando arquiteturas de aprendizado profundo
• Beneficiário: Sandra Eliza Fontes de Avila
• Modalidade de apoio: Auxílio à Pesquisa - Regular