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.

翻译：本文提出了一种面向图像与视频中面部匿名化的新方法，简称FIVA。该方法通过建议的身份追踪机制，能够在帧间保持一致的面部匿名化效果，并确保与原人脸存在显著差异。FIVA在误接受率为0.001时实现了零真阳性率。本研究着重考虑了重构攻击这一重要的安全问题，并探讨了利用对抗噪声、均匀噪声及参数噪声来破坏重构攻击。为此，我们应用了不同的防御与保护方法以应对这些隐私威胁，从而展示了FIVA的可扩展性。此外，我们还证明重构攻击模型可用于深度伪造检测。最后，通过实验结果表明，FIVA甚至能够实现仅凭单张目标图像训练的面部交换功能。