Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons
This addresses a safety issue for children exposed to harmful content on video platforms, and it is the first work in the literature on this specific problem.
The paper tackles the problem of detecting Elsagate content in cartoons, which features disturbing themes, by applying deep convolutional neural networks with static and motion information, achieving 92.6% accuracy.
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.