As more and more personal photos are shared and tagged in social media,
avoiding privacy risks such as unintended recognition becomes increasingly
challenging. We propose a new hybrid approach to obfuscate identities in photos
by head replacement. Our approach combines state of the art parametric face
synthesis with latest advances in Generative Adversarial Networks (GAN) for
data-driven image synthesis. On the one hand, the parametric part of our method
gives us control over the facial parameters and allows for explicit
manipulation of the identity. On the other hand, the data-driven aspects allow
for adding fine details and overall realism as well as seamless blending into
the scene context. In our experiments, we show highly realistic output of our
system that improves over the previous state of the art in obfuscation rate
while preserving a higher similarity to the original image content.

本研究提出了一种混合方法，通过头部替换来混淆照片中的身份信息，结合参数化人脸合成技术和生成对抗网络的最新进展，该方法可以对面部参数进行控制并允许对身份进行显式操作，并且可以添加细节和整体逼真度，实验结果表明该系统的输出相对原始图像的相似度更高，同时改进了遮蔽率。