FakeTracer:通过在训练中植入痕迹捕捉人脸交换深度假动作

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Pu Sun;Honggang Qi;Yuezun Li;Siwei Lyu
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引用次数: 0

摘要

DeepFake是一种新兴的基于人工智能的人脸伪造技术,它可以将视频中的原始人脸替换为目标身份的生成人脸,同时保持面部表情和方向等一致的面部属性。由于人脸的高度隐私性,滥用这种技术会引起严重的社会关注,最近引起了人们对DeepFakes的极大关注。在本文中,我们描述了一种新的主动防御方法,称为FakeTracer,通过在训练中植入痕迹来暴露人脸交换DeepFakes。与一般的人脸合成DeepFake相比,人脸交换DeepFake更加复杂,因为它涉及到身份的改变,并且经历了编解码过程,并且是无监督的训练,增加了在训练阶段植入痕迹的难度。为了有效防御人脸交换DeepFake,我们设计了两种类型的痕迹,可持续痕迹(STrace)和可擦除痕迹(ETrace),以添加到人脸训练中。在训练过程中,这些被操纵的人脸会影响人脸交换DeepFake模型的学习,使其能够生成只包含可持续痕迹的人脸。根据这两个痕迹,我们的方法可以通过识别它们来有效地暴露DeepFakes。大量的实验证实了我们的方法在防御人脸交换DeepFake方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FakeTracer: Catching Face-Swap DeepFakes via Implanting Traces in Training
Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation. Due to the high privacy of faces, the misuse of this technique can raise severe social concerns, drawing tremendous attention to defend against DeepFakes recently. In this article, we describe a new proactive defense method called FakeTracer to expose face-swap DeepFakes via implanting traces in training. Compared to general face-synthesis DeepFake, the face-swap DeepFake is more complex as it involves identity change, is subjected to the encoding-decoding process, and is trained unsupervised, increasing the difficulty of implanting traces into the training phase. To effectively defend against face-swap DeepFake, we design two types of traces, sustainable trace (STrace) and erasable trace (ETrace), to be added to training faces. During the training, these manipulated faces affect the learning of the face-swap DeepFake model, enabling it to generate faces that only contain sustainable traces. In light of these two traces, our method can effectively expose DeepFakes by identifying them. Extensive experiments corroborate the efficacy of our method on defending against face-swap DeepFake.
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来源期刊
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
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