对抗埃尔萨现象：用于扰乱漫画的深度学习架构
Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons
AI总结：
本文提出基于深度卷积神经网络的解决方案，用于检测漫画中的扰动内容，达到92.6%的准确率，旨在引发对埃尔萨现象的讨论。
摘要：
观看动画片对儿童的智力、社交和情绪发展有益。然而，当前最流行的视频分享平台提供大量包含埃尔萨现象的内容。埃尔萨现象描绘了儿童角色在令人不安的情境中（例如，血腥、厕所幽默、喝尿液、偷窃）。尽管这种威胁对儿童易于获取，但文献中尚无相关研究。作为首次探索漫画中扰动内容的研究，本文结合了最新的色情检测文献，应用深度卷积神经网络与视频的静态和动态信息。我们的解决方案兼容移动平台，达到了92.6%的准确率。我们的目标不仅是介绍首个解决方案，还旨在引发对埃尔萨现象的讨论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
