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Exploiting Style Latent Flows for Generalizing Deepfake Video Detection

Computing Research Repository (CoRR)(2024)

Chung-Ang University | NAVER | UNIST

Cited 30|Views44
Abstract
This paper presents a new approach for the detection of fake videos, based onthe analysis of style latent vectors and their abnormal behavior in temporalchanges in the generated videos. We discovered that the generated facial videossuffer from the temporal distinctiveness in the temporal changes of stylelatent vectors, which are inevitable during the generation of temporally stablevideos with various facial expressions and geometric transformations. Ourframework utilizes the StyleGRU module, trained by contrastive learning, torepresent the dynamic properties of style latent vectors. Additionally, weintroduce a style attention module that integrates StyleGRU-generated featureswith content-based features, enabling the detection of visual and temporalartifacts. We demonstrate our approach across various benchmark scenarios indeepfake detection, showing its superiority in cross-dataset andcross-manipulation scenarios. Through further analysis, we also validate theimportance of using temporal changes of style latent vectors to improve thegenerality of deepfake video detection.
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Key words
Deepfake Detection,Face Forgery Detection
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Chat Paper

要点】:本文提出了一种基于风格潜在向量及其在生成的视频中时间变化异常行为分析的假视频检测新方法,通过StyleGRU模块和风格注意力模块检测视觉和时间上的伪迹,提高了跨数据集和跨操作场景的检测性能。

方法】:采用基于对比学习的StyleGRU模块来表示风格潜在向量的动态特性,并引入风格注意力模块结合 StyleGRU生成的特征和基于内容的特征。

实验】:在多个深度伪造视频检测基准场景中验证了所提方法的有效性,结果表明其在跨数据集和跨操作场景的检测中具有优越性;通过进一步分析,验证了使用风格潜在向量的时间变化可以提高深度伪造视频检测的泛化能力。