TikGuard: A Deep Learning Transformer-Based Solution for Detecting Unsuitable TikTok Content for Kids

TL;DR

The paper addresses safeguarding children on TikTok by applying transformer-based video classification to a TikTok-specific dataset. TikGuard fine-tunes TimesFormer, ViViT, and VideoMAE on TikHarm to classify videos into Safe, Adult, Harmful, and Suicide. TimesFormer yields the strongest performance on validation and test sets, illustrating the value of temporal modeling for video moderation. This work lays groundwork for safer, child-friendly social platforms and points to future directions like robustness, broader datasets, and multimodal integration.

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.

Figures (3)

• Figure 1: Examples of each class in the TikHarm dataset.
• Figure 2: The preprocessing and augmentation pipeline
• Figure 3: The model fine-tuning process