  With the popularity of smart devices and the development of computer vision
technology, concerns about face privacy protection are growing. The face
de-identification technique is a practical way to solve the identity protection
problem. The existing facial de-identification methods have revealed several
problems, including the impact on the realism of anonymized results when faced
with occlusions and the inability to maintain identity-irrelevant details in
anonymized results. We present a High-Fidelity and Occlusion-Robust
De-identification (HFORD) method to deal with these issues. This approach can
disentangle identities and attributes while preserving image-specific details
such as background, facial features (e.g., wrinkles), and lighting, even in
occluded scenes. To disentangle the latent codes in the GAN inversion space, we
introduce an Identity Disentanglement Module (IDM). This module selects the
latent codes that are closely related to the identity. It further separates the
latent codes into identity-related codes and attribute-related codes, enabling
the network to preserve attributes while only modifying the identity. To ensure
the preservation of image details and enhance the network's robustness to
occlusions, we propose an Attribute Retention Module (ARM). This module
adaptively preserves identity-irrelevant details and facial occlusions and
blends them into the generated results in a modulated manner. Extensive
experiments show that our method has higher quality, better detail fidelity,
and stronger occlusion robustness than other face de-identification methods.
