利用发音表征学习进行音视频深度伪造检测

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yujia Wang, Hua Huang
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引用次数: 0

摘要

人工智能生成技术的进步使操作听觉和视觉元素变得更加容易,这就凸显了对稳健的视听深度防伪检测方法的迫切需要。在本文中,我们提出了一种基于发音表征的视听深度防伪检测方法 ART-AVDF。首先,我们设计了一个音频编码器来提取发音特征,捕捉发音运动的物理意义,并与唇音编码器集成,以自我监督学习的方式探索视听发音对应关系。然后,我们设计了一个多模态联合融合模块,利用发音嵌入进一步探索固有的视听一致性。在 DFDC、FakeAVCeleb 和 DefakeAVMiT 数据集上进行的大量实验表明,与许多深度防伪检测模型相比,ART-AVDF 的性能有了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Audio–visual deepfake detection using articulatory representation learning

Audio–visual deepfake detection using articulatory representation learning

Advancements in generative artificial intelligence have made it easier to manipulate auditory and visual elements, highlighting the critical need for robust audio–visual deepfake detection methods. In this paper, we propose an articulatory representation-based audio–visual deepfake detection approach, ART-AVDF. First, we devise an audio encoder to extract articulatory features that capture the physical significance of articulation movement, integrating with a lip encoder to explore audio–visual articulatory correspondences in a self-supervised learning manner. Then, we design a multimodal joint fusion module to further explore inherent audio–visual consistency using the articulatory embeddings. Extensive experiments on the DFDC, FakeAVCeleb, and DefakeAVMiT datasets demonstrate that ART-AVDF obtains a significant performance improvement compared to many deepfake detection models.

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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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