The rapid progress of generative AI and the explosive popularity of short-form video platforms (e.g., TikTok, YouTube Shorts) have made it easier than ever to create and spread vivid, persuasive misinformation videos. Detecting and preventing such content has become a critical challenge for the health of online multimedia ecosystems.
This half-day tutorial systematically covers:
We conclude with open challenges and future research directions. The tutorial is designed for attendees with basic knowledge of multimedia forensics and generative AI.
All times are local (Dublin, IST).
| Time | Section | Presenter |
|---|---|---|
| / | Introduction & Motivation | Qiang Sheng |
| / | Preliminaries: Video Editing & Generation | Qiang Sheng |
| / | Characterization of Misinformation Videos | Qiang Sheng |
| / | Detection Part I: AI-Generated Misinformation | Tianyun Yang |
| / | Detection Part II: Human-Edited Misinformation | Yuyan Bu |
| / | Coffee Break | |
| / | Prevention Strategies | Peng Qi |
| / | Conclusion & Open Discussion | Qiang Sheng |
Key papers and datasets will be listed on the tutorial webpage. Stay tuned!
@inproceedings{mm25tutorial,
title={Combating Online Misinformation Videos: Characterization, Detection, and Prevention},
author={Sheng, Qiang and Qi, Peng and Yang, Tianyun and Bu, Yuyan and Hsu, Wynne and Lee, Mong Li and Cao, Juan},
booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
year={2025},
doi={10.1145/3746027.3759206},
publisher={ACM}
}