Deepfake detection with domain generalization and mask-guided supervision

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jicheng Li , Yongjian Hu , Beibei Liu , Huimin She , Chang-Tsun Li
{"title":"Deepfake detection with domain generalization and mask-guided supervision","authors":"Jicheng Li ,&nbsp;Yongjian Hu ,&nbsp;Beibei Liu ,&nbsp;Huimin She ,&nbsp;Chang-Tsun Li","doi":"10.1016/j.patcog.2025.111622","DOIUrl":null,"url":null,"abstract":"<div><div>Most existing deepfake (video face forgery) detectors work well in intra-dataset testing, but their performance degrades severely in cross-dataset testing. Cross-dataset generalization remains a major challenge. Since domain generalization (DG) aims to learn domain-invariant features while suppressing domain specific features, we propose a DG framework for improving face forgery detection in this study. Our detector consists of two modules. The first module learns both spatial and spectral features from frame images. The second one learns high-level feature patterns from the outputs of the first module, and constructs the classification features with the help of face mask-guided supervision. The classification result is fine-tuned by a confidence-based correction mechanism. The DG framework is realized through a bi-level optimization process. Extensive experiments demonstrate that our detector works effectively in both intra- and cross-dataset testing. Compared with 8 typical methods, it has the best overall performance and the highest robustness against common perturbations.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"165 ","pages":"Article 111622"},"PeriodicalIF":7.5000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320325002821","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Most existing deepfake (video face forgery) detectors work well in intra-dataset testing, but their performance degrades severely in cross-dataset testing. Cross-dataset generalization remains a major challenge. Since domain generalization (DG) aims to learn domain-invariant features while suppressing domain specific features, we propose a DG framework for improving face forgery detection in this study. Our detector consists of two modules. The first module learns both spatial and spectral features from frame images. The second one learns high-level feature patterns from the outputs of the first module, and constructs the classification features with the help of face mask-guided supervision. The classification result is fine-tuned by a confidence-based correction mechanism. The DG framework is realized through a bi-level optimization process. Extensive experiments demonstrate that our detector works effectively in both intra- and cross-dataset testing. Compared with 8 typical methods, it has the best overall performance and the highest robustness against common perturbations.
基于域泛化和掩码引导监督的深度伪造检测
大多数现有的深度伪造(视频人脸伪造)检测器在数据集内测试中表现良好,但在跨数据集测试中性能下降严重。跨数据集泛化仍然是一个主要的挑战。由于领域泛化(DG)旨在学习领域不变特征,同时抑制领域特定特征,因此本文提出了一种改进人脸伪造检测的DG框架。我们的检测器由两个模块组成。第一个模块从帧图像中学习空间和光谱特征。第二个模块从第一个模块的输出中学习高级特征模式,并借助面罩引导监督构建分类特征。分类结果通过基于置信度的校正机制进行微调。DG框架是通过双层优化过程实现的。大量的实验表明,我们的检测器在内部和跨数据集测试中都能有效地工作。与8种典型方法相比,该方法具有最佳的综合性能和对常见扰动的最高鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信