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
this https URL

提出 UMMAFormer，一种新颖的通用 Transformer 框架，用于时序伪造定位，通过多模态适应预测伪造段，包括引入基于时序特征重建的时序特征异常关注模块（TFAA）以增强时序差异的检测，设计用于微妙特征增强的平行交叉关注特征金字塔网络（PCA-FPN），并构建适用于视频修复场景的新型时序视频修复定位（TVIL）数据集，实验结果表明我们的方法在众多基准数据集上取得了最优性能。