# Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons Source: [Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons]() Collection index: [llms.txt]() Data updated: 2026-04-22T05:22:24.042Z Authors: Akari Ishikawa, Edson Bollis, Sandra Avila Published: 2019-04-18T17:43:42.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Video Classification on Elsagate (train)](): Video Classification; Elsagate (train); Accuracy: 96.1. - [Video Classification on Elsagate (test)](): Video Classification; Elsagate (test); Accuracy: 92.6. ## Original sources and implementation [Original paper]() [PDF]()