Rapid development and application of diffusion models has led to a surge in diffusion generated video content, increasing the risk of fake information spreading. Therefore, it is imperative to develop a generalized and robust detector for diffusion generated videos. However, existing detection methods lack generalized and robust representations, failing to generalize across videos created by multiple diffusion models. In this paper, we point out that text-vision embedding provides a generalized and robust representation for diffusion generated video detection, building on the success of text-image contrastive learning. Visual embeddings from text-visual contrastive pre-trained models inherently separate real and diffusion generated videos. Leveraging this insight, we propose a generalized detection framework that exploits contrastive embeddings between visual content and text semantics to capture generation artifacts. A comprehensive evaluation across multiple video generation models shows our detector’s superior generalized capability. On unseen diffusion video models, it achieves an average accuracy 0.9229. The code is available at https://github.com/1129ljc/T2VE .