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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1904.08910 |
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| _version_ | 1866915942419660800 |
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| author | Ishikawa, Akari Bollis, Edson Avila, Sandra |
| author_facet | Ishikawa, Akari Bollis, Edson Avila, Sandra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1904_08910 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons Ishikawa, Akari Bollis, Edson Avila, Sandra Computer Vision and Pattern Recognition 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. |
| title | Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1904.08910 |