research-article

Constructing Spatio-Temporal Graphs for Face Forgery Detection

Authors:

Yongdong ZhangAuthors Info & Claims

ACM Transactions on the Web, Volume 17, Issue 3

Article No.: 23, Pages 1 - 25

https://doi.org/10.1145/3580512

Published: 22 May 2023 Publication History

Abstract

Recently, advanced development of facial manipulation techniques threatens web information security, thus, face forgery detection attracts a lot of attention. It is clear that both spatial and temporal information of facial videos contains the crucial manipulation traces, which are inevitably created during the generation process. However, most existing face forgery detectors only focus on the spatial artifacts or the temporal incoherence, and they are struggling to learn a significant and general kind of representations for manipulated facial videos. In this work, we propose to construct spatial-temporal graphs for fake videos to capture the spatial inconsistency and the temporal incoherence at the same time. To model the spatial-temporal relationship among the graph nodes, a novel forgery detector named Spatio-Temporal Graph Network (STGN) is proposed, which contains two kinds of graph-convolution-based units, the Spatial Relation Graph Unit (SRGU) and the Temporal Attention Graph Unit (TAGU). To exploit spatial information, the SRGU models the inconsistency between each pair of patches in the same frame, instead of focusing on the low-level local spatial artifacts which are vulnerable to samples created by unseen manipulation methods. And, the TAGU is proposed to model the long-distance temporal relation among the patches at the same spatial position in different frames with a graph attention mechanism based on the inter-node similarity. With the SRGU and the TAGU, our STGN can combine the discriminative power of spatial inconsistency and the generalization capacity of temporal incoherence for face forgery detection. Our STGN achieves state-of-the-art performances on several popular forgery detection datasets. Extensive experiments demonstrate both the superiority of our STGN on intra manipulation evaluation and the effectiveness for new sorts of face forgery videos on cross manipulation evaluation.

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Cited By

Peng CMiao ZLiu DWang NHu RGao X(2024)Where Deepfakes Gaze at? Spatial–Temporal Gaze Inconsistency Analysis for Video Face Forgery DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338182319(4507-4517)Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3381823
Naitali ARidouani MSalahdine FKaabouch N(2023)Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research DirectionsComputers10.3390/computers1210021612:10(216)Online publication date: 23-Oct-2023
https://doi.org/10.3390/computers12100216
Fang MYu LXie HWu JWang ZLi JZhang YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)RAIRNet: Region-Aware Identity Rectification for Face Forgery DetectionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612321(1455-1464)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612321
Show More Cited By

Index Terms

Constructing Spatio-Temporal Graphs for Face Forgery Detection
1. Applied computing
  1. Computer forensics
2. Information systems
  1. World Wide Web
    1. Web mining

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 17, Issue 3

August 2023

302 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3597636

Editor:
Ryen White
Microsoft Research, USA

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Publication History

Published: 22 May 2023

Online AM: 30 January 2023

Accepted: 20 October 2022

Revised: 27 June 2022

Received: 31 January 2022

Published in TWEB Volume 17, Issue 3

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Cited By

Peng CMiao ZLiu DWang NHu RGao X(2024)Where Deepfakes Gaze at? Spatial–Temporal Gaze Inconsistency Analysis for Video Face Forgery DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338182319(4507-4517)Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3381823
Naitali ARidouani MSalahdine FKaabouch N(2023)Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research DirectionsComputers10.3390/computers1210021612:10(216)Online publication date: 23-Oct-2023
https://doi.org/10.3390/computers12100216
Fang MYu LXie HWu JWang ZLi JZhang YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)RAIRNet: Region-Aware Identity Rectification for Face Forgery DetectionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612321(1455-1464)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612321
Waseem SAbu Bakar SAhmed BOmar ZEisa TDalam M(2023)DeepFake on Face and Expression Swap: A ReviewIEEE Access10.1109/ACCESS.2023.332440311(117865-117906)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3324403

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