CH-SV: A Benchmark for Multi-Type Chinese Harmful Short Video Detection

摘要

Short video platforms are popular for sharing information, but they also spread harmful content quickly. Research on detecting harmful videos is limited due to unclear categories and a lack of good datasets. To solve this, we created CH-SV, the first Chinese Harmful Short Video dataset with 6,728 videos labeled into six categories: danger, offense, vulgarity, fakeness, violence, and normal content. We analyzed CH-SV in detail and proposed HAVE, a new detection framework that improves video understanding using AI-generated semantics. Experiments on CH-SV show that our dataset can help advance harmful video detection research, and HAVE outperforms existing methods. Resources including core code, dataset samples, supplementary material, and licensing details are available at https://github.com/DLUTSSL/CH-SV.