# Malicious or Benign? Towards Effective Content Moderation for Children's Videos

arXiv: https://arxiv.org/abs/2305.15551 · HTML: https://mlsanity.com/paper/2305.15551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Video Transformer Network](https://mlsanity.com/paper/2102.00719/) (2021)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children](https://mlsanity.com/paper/1901.07046/) (2019)
- [Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube](https://mlsanity.com/paper/2103.09050/) (2021)
- [YouTubers Not madeForKids: Detecting Channels Sharing Inappropriate Videos Targeting Children](https://mlsanity.com/paper/2205.13885/) (2022)
- [KidsTube: Detection, Characterization and Analysis of Child Unsafe Content & Promoters on YouTube](https://mlsanity.com/paper/1608.05966/) (2016)
- [Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons](https://mlsanity.com/paper/1904.08910/) (2019)
- [Did your child get disturbed by an inappropriate advertisement on YouTube?](https://mlsanity.com/paper/2211.02356/) (2022)

## Built on by (most cited first, 5 of 5)

- [SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations](https://mlsanity.com/paper/2412.06878/) (2024)
- [VideoEval: Comprehensive Benchmark Suite for Low-Cost Evaluation of Video Foundation Model](https://mlsanity.com/paper/2407.06491/) (2024)
- [Enhanced Multimodal Content Moderation of Children's Videos using Audiovisual Fusion](https://mlsanity.com/paper/2405.06128/) (2024)
- [The Potential of Vision-Language Models for Content Moderation of Children's Videos](https://mlsanity.com/paper/2312.03936/) (2023)
- [Child-Oriented AIGC Video Risk Reviewing: A Benchmark and Knowledge-Supported Iterative Reasoning Framework](https://mlsanity.com/paper/2607.22715/) (2026)

## Abstract (arXiv)

Online video platforms receive hundreds of hours of uploads every minute, making manual content moderation impossible. Unfortunately, the most vulnerable consumers of malicious video content are children from ages 1-5 whose attention is easily captured by bursts of color and sound. Scammers attempting to monetize their content may craft malicious children's videos that are superficially similar to educational videos, but include scary and disgusting characters, violent motions, loud music, and disturbing noises. Prominent video hosting platforms like YouTube have taken measures to mitigate malicious content on their platform, but these videos often go undetected by current content moderation tools that are focused on removing pornographic or copyrighted content. This paper introduces our toolkit Malicious or Benign for promoting research on automated content moderation of children's videos. We present 1) a customizable annotation tool for videos, 2) a new dataset with difficult to detect test cases of malicious content and 3) a benchmark suite of state-of-the-art video classification models.