  With the identity information in face data more closely related to personal
credit and property security, people pay increasing attention to the protection
of face data privacy. In different tasks, people have various requirements for
face de-identification (De-ID), so we propose a systematical solution
compatible for these De-ID operations. Firstly, an attribute disentanglement
and generative network is constructed to encode two parts of the face, which
are the identity (facial features like mouth, nose and eyes) and expression
(including expression, pose and illumination). Through face swapping, we can
remove the original ID completely. Secondly, we add an adversarial vector
mapping network to perturb the latent code of the face image, different from
previous traditional adversarial methods. Through this, we can construct
unrestricted adversarial image to decrease ID similarity recognized by model.
Our method can flexibly de-identify the face data in various ways and the
processed images have high image quality.
