In this paper, we present a new approach for facial anonymization in images
and videos, abbreviated as FIVA. Our proposed method is able to maintain the
same face anonymization consistently over frames with our suggested
identity-tracking and guarantees a strong difference from the original face.
FIVA allows for 0 true positives for a false acceptance rate of 0.001. Our work
considers the important security issue of reconstruction attacks and
investigates adversarial noise, uniform noise, and parameter noise to disrupt
reconstruction attacks. In this regard, we apply different defense and
protection methods against these privacy threats to demonstrate the scalability
of FIVA. On top of this, we also show that reconstruction attack models can be
used for detection of deep fakes. Last but not least, we provide experimental
results showing how FIVA can even enable face swapping, which is purely trained
on a single target image.

本文介绍一种新的面部匿名化方法，名为 FIVA，提出了使用身份追踪实现一致的面部匿名化，并保证与原始面部有很强的区别。FIVA 对于 0.001 的误通过率具有 0 的真阳性。我们的工作考虑到了重建攻击这一重要的安全问题，并对对抗性噪声、均匀噪声和参数噪声应用不同的防御和保护方法来破坏重建攻击。此外，我们还展示了重建攻击模型可以用于检测深度伪造。最后，我们提供了实验证据，展示了 FIVA 如何甚至能够在单个目标图像上进行面部交换。