TransHFC: Joints Hypergraph Filtering Convolution and Transformer Framework for Temporal Forgery Localization

Jiahao Huang,Xiaochen Yuan, Chan-Tong Lam,Sio-Kei Im,Fangyuan Lei,Xiuli Bi

IEEE Transactions on Circuits and Systems for Video Technology（2025）

Faculty of Applied Sciences | Macao Polytechnic University

Cited 0|Views5

Abstract

The authenticity of audio-visual content is being challenged by advanced multimedia editing technologies inspired by Artificial Intelligence-Generated Content (AIGC). Temporal forgery localization aims to detect suspicious contents by locating forged segments. So far, most of the existing methods are based on Convolutional Neural Networks (CNNs) or Transformers, yet neither of them has fully considered the complex relationships within forged audio-visual content. To address this issue, in this paper, we propose a novel method, named TransHFC, which innovatively introduces hypergraphs to model group relationships among segments while considering point-to-point relationships through Transformers. Through its dual hypergraph filtering convolution branch, TransHFC captures both temporal and spatial level group relationships, enhancing the representation of forged segment features. Furthermore, we propose a new hypergraph filtering convolution Auto-Encoder that uses a multi-frequency filter bank for adaptive signal capture. This design compensates for the limitation of a single hypergraph filter. Our extensive experiments on Lav-DF, TVIL, Psynd, and HAD datasets demonstrate that TransHFC achieves state-of-the-art performance.

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Key words

Temporal Forgery Localization,Hypergraph,Transformer,Hypergraph Convolution

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【要点】：本文提出了TransHFC方法，一种结合超图滤波卷积和Transformer框架的新颖算法，用于时间伪造内容的定位，有效捕捉了音视频内容中复杂的伪造片段关系。

【方法】：TransHFC通过引入超图来建模片段间的群组关系，同时利用Transformer处理点对点关系，采用双超图滤波卷积分支来增强对伪造片段特征的表示。

【实验】：在Lav-DF、TVIL、Psynd和HAD数据集上进行的广泛实验表明，TransHFC达到了最先进的性能水平。