3D data augmentation and dual-branch model for robust face forgery detection

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models Pub Date : 2025-02-04 DOI:10.1016/j.gmod.2025.101255

Changshuang Zhou , Frederick W.B. Li , Chao Song , Dong Zheng , Bailin Yang

引用次数: 0

Abstract

We propose Dual-Branch Network (DBNet), a novel deepfake detection framework that addresses key limitations of existing works by jointly modeling 3D-temporal and fine-grained texture representations. Specifically, we aim to investigate how to (1) capture dynamic properties and spatial details in a unified model and (2) identify subtle inconsistencies beyond localized artifacts through temporally consistent modeling. To this end, DBNet extracts 3D landmarks from videos to construct temporal sequences for an RNN branch, while a Vision Transformer analyzes local patches. A Temporal Consistency-aware Loss is introduced to explicitly supervise the RNN. Additionally, a 3D generative model augments training data. Extensive experiments demonstrate our method achieves state-of-the-art performance on benchmarks, and ablation studies validate its effectiveness in generalizing to unseen data under various manipulations and compression.

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三维数据增强和双分支模型鲁棒人脸伪造检测

我们提出了双分支网络（DBNet），这是一种新的深度伪造检测框架，通过联合建模3D-temporal和细粒度纹理表示来解决现有工作的关键限制。具体来说，我们的目标是研究如何(1)在统一的模型中捕获动态属性和空间细节；(2)通过时间一致的建模来识别局部工件之外的细微不一致。为此，DBNet从视频中提取3D地标来构建RNN分支的时间序列，而Vision Transformer则分析局部补丁。引入了时间一致性感知损失来显式地监督RNN。此外，3D生成模型增强了训练数据。大量的实验表明，我们的方法在基准测试中达到了最先进的性能，消融研究证实了它在各种操作和压缩下推广到未见数据的有效性。

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来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.