UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for
  Temporal Forgery Localization
Rui Zhang Hongxia Wang Mingshan Du Hanqing Liu Yang Zhou Qiang Zeng
The emergence of artificial intelligence-generated content (AIGC) has raised
concerns about the authenticity of multimedia content in various fields.
However, existing research for forgery content detection has focused mainly on
binary classification tasks of complete videos, which has limited applicability
in industrial settings. To address this gap, we propose UMMAFormer, a novel
universal transformer framework for temporal forgery localization (TFL) that
predicts forgery segments with multimodal adaptation. Our approach introduces a
Temporal Feature Abnormal Attention (TFAA) module based on temporal feature
reconstruction to enhance the detection of temporal differences. We also design
a Parallel Cross-Attention Feature Pyramid Network (PCA-FPN) to optimize the
Feature Pyramid Network (FPN) for subtle feature enhancement. To evaluate the
proposed method, we contribute a novel Temporal Video Inpainting Localization
(TVIL) dataset specifically tailored for video inpainting scenes. Our
experiments show that our approach achieves state-of-the-art performance on
benchmark datasets, including Lav-DF, TVIL, and Psynd, significantly
outperforming previous methods. The code and data are available at
https://github.com/ymhzyj/UMMAFormer/.