用Transformer模型检测TikTok儿童不宜内容，准确率达86.7%。
TikGuard: A Deep Learning Transformer-Based Solution for Detecting Unsuitable TikTok Content for Kids
- 基于Transformer架构，结合自建数据集TikHarm进行训练。
- 在儿童不宜内容检测任务中达到86.7%准确率。
- 适合关注青少年网络保护与视频审核技术的研究者。
TikTok等短视频平台的兴起给保护年轻观众免受不当内容侵害带来了新挑战。传统审核方法难以应对海量且快速变化的用户生成视频，增加了儿童接触有害内容的风险。本文提出TikGuard，一种基于Transformer的深度学习方法，用于检测和标记TikTok上不适合儿童的内容。通过使用专门构建的数据集TikHarm，并结合先进的视频分类技术，TikGuard在测试中实现了86.7%的准确率，相比现有方法有明显提升。尽管因TikHarm数据集的独特性导致直接对比有限，但其表现证明了Transformer模型在视频分类中的有效性，为未来相关研究奠定了基础。
原文摘要 · Abstract (English)
The rise of short-form videos on platforms like TikTok has brought new challenges in safeguarding young viewers from inappropriate content. Traditional moderation methods often fall short in handling the vast and rapidly changing landscape of user-generated videos, increasing the risk of children encountering harmful material. This paper introduces TikGuard, a transformer-based deep learning approach aimed at detecting and flagging content unsuitable for children on TikTok. By using a specially curated dataset, TikHarm, and leveraging advanced video classification techniques, TikGuard achieves an accuracy of 86.7%, showing a notable improvement over existing methods in similar contexts. While direct comparisons are limited by the uniqueness of the TikHarm dataset, TikGuard's performance highlights its potential in enhancing content moderation, contributing to a safer online experience for minors. This study underscores the effectiveness of transformer models in video classification and sets a foundation for future research in this area.
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