论文标题

研究深击视频中检测行为特征的研究

Study of detecting behavioral signatures within DeepFake videos

论文作者

Miao, Qiaomu, Kang, Sinhwa, Marsella, Stacy, DiPaola, Steve, Wang, Chao, Shapiro, Ari

论文摘要

人们对出于各种目的的人交谈的综合视频图像产生了浓厚的兴趣,包括娱乐,沟通,培训和广告。随着深层伪造生成模型的发展,合成视频图像很快将在视觉上与自然捕获视频的肉眼无法区分。此外,许多方法正在继续改进,以避免更谨慎,法医视觉分析。通过使用面部木偶来制作一些深层的虚假视频,该视频通过演员的动作直接控制合成图像的头部和面部,使演员可以将其“木偶”“木偶”“木偶”“木偶”。在本文中,我们解决了一个问题,即是否可以通过控制扬声器的视觉外观,但将行为信号从另一个来源转移到原始扬声器中。我们通过比较综合图像来进行研究:1)源自另一个人说不同话语的人,2)起源于同一人说的话不同,3)源自另一个人说相同话语的人。我们的研究表明,在所有三种情况下,合成视频都比原始源视频不那么真实和吸引力。我们的结果表明,可以从一个人的动作中检测到​​与视觉外观分开的行为签名,并且可以使用这种行为签名来区分深处的伪造与正确捕获的视频。

There is strong interest in the generation of synthetic video imagery of people talking for various purposes, including entertainment, communication, training, and advertisement. With the development of deep fake generation models, synthetic video imagery will soon be visually indistinguishable to the naked eye from a naturally capture video. In addition, many methods are continuing to improve to avoid more careful, forensic visual analysis. Some deep fake videos are produced through the use of facial puppetry, which directly controls the head and face of the synthetic image through the movements of the actor, allow the actor to 'puppet' the image of another. In this paper, we address the question of whether one person's movements can be distinguished from the original speaker by controlling the visual appearance of the speaker but transferring the behavior signals from another source. We conduct a study by comparing synthetic imagery that: 1) originates from a different person speaking a different utterance, 2) originates from the same person speaking a different utterance, and 3) originates from a different person speaking the same utterance. Our study shows that synthetic videos in all three cases are seen as less real and less engaging than the original source video. Our results indicate that there could be a behavioral signature that is detectable from a person's movements that is separate from their visual appearance, and that this behavioral signature could be used to distinguish a deep fake from a properly captured video.

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