标志断路器：通过干扰标志提取来阻碍DeepFake
Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction
摘要：
深度神经网络（DNN）的最新发展显着提高了人工智能合成人脸的真实性，最显着的例子是DeepFakes。DeepFake技术可以从另一个主体的面部合成目标主体的面部，同时保留相同的面部属性。随着社交媒体门户网站（Facebook，Instagram等）的迅速增加，这些逼真的假面孔通过互联网迅速传播，对社会造成了广泛的负面影响。在本文中，我们描述了Landmark Breaker，这是第一个专门用于破坏面部标志提取的方法，并将其应用于阻碍DeepFake视频的生成。我们的动机是，破坏面部标志提取会影响输入面部的对齐，从而降低DeepFake质量。我们的方法是使用对抗扰动实现的。与仅在DeepFake生成后才起作用的检测方法相比，Landmark Breaker提前一步阻止DeepFake生成。实验进行了三个国家的最先进的面部标志提取器使用最近的Celeb-DF数据集。
英文摘要：
The recent development of Deep Neural Networks (DNN) has significantly increased the realism of AI-synthesized faces, with the most notable examples being the DeepFakes. The DeepFake technology can synthesize a face of target subject from a face of another subject, while retains the same face attributes. With the rapidly increased social media portals (Facebook, Instagram, etc), these realistic fake faces rapidly spread though the Internet, causing a broad negative impact to the society. In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos.Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality. Our method is achieved using adversarial perturbations. Compared to the detection methods that only work after DeepFake generation, Landmark Breaker goes one step ahead to prevent DeepFake generation. The experiments are conducted on three state-of-the-art facial landmark extractors using the recent Celeb-DF dataset.
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