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most citedOn Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection

1 citations · 1 across the 11 of their papers we have counts for

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cs.CV2026

Reading Seeing: Diagnosing and Closing the Typography Gap in Vision-Language Models

Heng Zhou, Ao Yu, Li Kang +5

Vision-Language Models achieve near-perfect accuracy at reading text in images, yet prove largely typography-blind: capable of recognizing what text says, but not how it looks. We…

cs.CV2025

Consolidating Diffusion-Generated Video Detection with Unified Multimodal Forgery Learning

Xiaohong Liu, Xiufeng Song, Huayu Zheng +3

The proliferation of videos generated by diffusion models has raised increasing concerns about information security, highlighting the urgent need for reliable detection of syntheti…

cs.CV2025

UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines

Chen Tang, Xinzhu Ma, Encheng Su +6

Traditional spatiotemporal models generally rely on task-specific architectures, which limit their generalizability and scalability across diverse tasks due to domain-specific desi…

cs.CV2025

Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector

Xiao Guo, Xiufeng Song, Yue Zhang +2

Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification…

cs.CV2024★ 1 cited

On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection

Xiufeng Song, Xiao Guo, Jiache Zhang +5

Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection. Ho…