In this work, we present Facial Identity Controllable GAN (FICGAN) for not
only generating high-quality de-identified face images with ensured privacy
protection, but also detailed controllability on attribute preservation for
enhanced data utility. We tackle the less-explored yet desired functionality in
face de-identification based on the two factors. First, we focus on the
challenging issue to obtain a high level of privacy protection in the
de-identification task while uncompromising the image quality. Second, we
analyze the facial attributes related to identity and non-identity and explore
the trade-off between the degree of face de-identification and preservation of
the source attributes for enhanced data utility. Based on the analysis, we
develop Facial Identity Controllable GAN (FICGAN), an autoencoder-based
conditional generative model that learns to disentangle the identity attributes
from non-identity attributes on a face image. By applying the manifold k-same
algorithm to satisfy k-anonymity for strengthened security, our method achieves
enhanced privacy protection in de-identified face images. Numerous experiments
demonstrate that our model outperforms others in various scenarios of face
de-identification.

本文提出了一种基于自编码器的条件生成模型 Facial Identity Controllable GAN (FICGAN)，利用 k-same 算法对面部图像进行去识别化处理，保证了隐私保护的同时提高了数据利用率。