用多模态模型检测社交平台假内容，效果好且可解释。
Detecting AI-Generated Content on Social Media with Multi-modal Language Models
- 构建持续更新的多模态数据集，训练轻量级视觉语言模型。
- 在公开数据集上达到顶尖检测效果，在内部数据上表现稳健。
- 已上线真实社交平台，提升用户互动，适合内容安全场景。
生成式AI使逼真的图像和视频在社交媒体上广泛传播，常被用于垃圾信息、虚假宣传、操纵和欺诈。现有AI生成内容（AIGC）检测方法存在泛化能力差、依赖单一模态、缺乏可解释性等挑战。本文提出一个持续收集多样化多模态社交媒体数据并训练紧凑型视觉-语言模型的检测与解释流程。该模型在公开基准上达到当前最优性能，并在多个平台的内部数据集上展现出稳健的检测与解释能力。我们已在社交平台部署该模型用于内容推荐，观察到用户参与度显著提升，证明了其在动态真实环境中的有效性。
原文摘要 · Abstract (English)
Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, and fraud. Existing AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, and lack of interpretable explanations. We present our pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model for detection and explanation. Our model achieves state-of-the-art detection performance on public benchmarks and demonstrates robust detection and explanation capabilities on internal social media datasets across multiple platforms. We deployed our model for post recommendation on social media platforms and observed positive downstream impacts on user engagement, demonstrating that it is feasible to perform effective AIGC detection in dynamic, real-world social media environments.
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