用多模态技术实时识别TikTok有害内容，保护儿童上网安全。
MTikGuard System: A Transformer-Based Multimodal System for Child-Safe Content Moderation on TikTok
- 构建融合视觉、音频、文本的多模态检测框架
- 在4723个视频上达到89.37%准确率和89.45%F1分数
- 支持实时部署，适合平台级内容审核场景
随着短视频兴起，TikTok已成为儿童青少年最主流的社交平台之一，但同时也充斥着可能影响其认知与行为的有害内容。这类内容往往隐蔽且具有欺骗性，传统方法难以应对海量实时上传带来的挑战。本文提出MTikGuard系统，实现TikTok平台的实时多模态有害内容检测，主要贡献包括：(1) 扩展TikHarm数据集至4,723个标注视频，涵盖更多真实世界样本；(2) 设计整合视觉、音频与文本特征的多模态分类框架，在测试中取得89.37%准确率与89.45% F1-score，达到当前最佳性能；(3) 基于Apache Kafka与Apache Spark构建可扩展的流式架构，支持大规模实时部署。实验表明，结合数据集扩充、先进多模态融合与稳健部署方案，能有效提升实际应用场景下的内容审核效果。数据集已开源：https://github.com/ntdat-8324/MTikGuard-System.git。
原文摘要 · Abstract (English)
With the rapid rise of short-form videos, TikTok has become one of the most influential platforms among children and teenagers, but also a source of harmful content that can affect their perception and behavior. Such content, often subtle or deceptive, challenges traditional moderation methods due to the massive volume and real-time nature of uploads. This paper presents MTikGuard, a real-time multimodal harmful content detection system for TikTok, with three key contributions: (1) an extended TikHarm dataset expanded to 4,723 labeled videos by adding diverse real-world samples, (2) a multimodal classification framework integrating visual, audio, and textual features to achieve state-of-the-art performance with 89.37% accuracy and 89.45% F1-score, and (3) a scalable streaming architecture built on Apache Kafka and Apache Spark for real-time deployment. The results demonstrate the effectiveness of combining dataset expansion, advanced multimodal fusion, and robust deployment for practical large-scale social media content moderation. The dataset is available at https://github.com/ntdat-8324/MTikGuard-System.git.
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