A Systematical Solution for Face De-identification
This addresses privacy protection in face data for applications like security and personal credit, though it appears incremental as it builds on existing de-identification techniques.
The paper tackles the problem of flexible face de-identification by proposing a systematic solution that combines attribute disentanglement for face swapping and an adversarial vector mapping network to reduce identity similarity, resulting in high-quality processed images.
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.