利用面部关系和特征聚合进行多人脸伪造检测

IF 6.3 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Chenhao Lin;Fangbin Yi;Hang Wang;Jingyi Deng;Zhengyu Zhao;Qian Li;Chao Shen
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引用次数: 0

摘要

先进的 "深度伪造 "技术的出现逐渐引起了社会的关注,促使人们开始重视 "深度伪造 "检测。然而,在现实场景中,Deepfake 往往涉及多张人脸。尽管如此,现有的大多数检测方法仍然是单独检测这些人脸,忽略了他们之间的信息关联性以及图像的全局信息与人脸的局部信息之间的关系。在本文中,我们针对这一局限性提出了 FILTER,这是一种用于多人脸伪造检测的新型框架,能明确捕捉潜在的相关性。FILTER 由两个主要模块组成:多人脸关系学习(MRL)和全局特征聚合(GFA)。具体来说,MRL 学习多面图像中局部面部特征的相关性,而 GFA 则构建图像级标签与单个面部特征之间的关系,以从全局角度提高性能。特别是,对比学习损失函数用于更好地区分真假人脸。在两个公开的多人脸伪造数据集上进行的大量实验证明,FILTER 在多人脸伪造检测方面具有一流的性能。例如,在 Openforensics Test-Challenge 数据集上,FILTER 以更高的 AUC 得分(0.980)和更高的检测准确率(92.04%)超越了之前的先进方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
The emergence of advanced Deepfake technologies has gradually raised concerns in society, prompting significant attention to Deepfake detection. However, in real-world scenarios, Deepfakes often involve multiple faces. Despite this, most existing detection methods still detect these faces individually, overlooking the informative correlation between them and the relationship between the global information of the image and the local information of the faces. In this paper, we address this limitation by proposing FILTER, a novel framework for multi-face forgery detection that explicitly captures underlying correlations. FILTER consists of two main modules: Multi-face Relationship Learning (MRL) and Global Feature Aggregation (GFA). Specifically, MRL learns the correlation of local facial features in multi-face images, and GFA constructs the relationship between image-level labels and individual facial features to enhance performance from a global perspective. In particular, a contrastive learning loss function is used to better discriminate between real and fake faces. Extensive experiments on two publicly available multi-face forgery datasets demonstrate the state-of-the-art performance of FILTER in multi-face forgery detection. For example, on Openforensics Test-Challenge dataset, FILTER outperforms the previous state-of-the-art methods with a higher AUC score (0.980) and higher detection accuracy (92.04%).
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来源期刊
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security 工程技术-工程:电子与电气
CiteScore
14.40
自引率
7.40%
发文量
234
审稿时长
6.5 months
期刊介绍: The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features
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