Abstract
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.
Abstract (translated)
短形式视频在像TikTok这样的平台上崛起，为保护年轻观众免受不良内容带来了新的挑战。传统的审核方法通常在处理用户生成视频的广泛且快速变化的地形方面存在缺陷，增加了孩子们接触有害材料的风险。本文介绍了TikGuard，一种基于Transformer的深度学习方法，旨在检测和标记TikTok上不适合儿童的内容。通过使用专门挑选的数据集TikHarm和利用先进的视频分类技术，TikGuard实现了86.7%的准确率，表明其在类似环境中与现有方法相比取得了显著的改善。尽管直接比较受到TikHarm数据集独特性的限制，但TikGuard的表现突出了其在增强内容审核方面的潜力，为未成年人提供更安全在线体验。本研究突出了Transformer模型在视频分类中的有效性，为该领域未来的研究奠定了基础。
URL
https://arxiv.org/abs/2410.00403
