  Because of the explosive growth of face photos as well as their widespread
dissemination and easy accessibility in social media, the security and privacy
of personal identity information becomes an unprecedented challenge. Meanwhile,
the convenience brought by advanced identity-agnostic computer vision
technologies is attractive. Therefore, it is important to use face images while
taking careful consideration in protecting people's identities. Given a face
image, face de-identification, also known as face anonymization, refers to
generating another image with similar appearance and the same background, while
the real identity is hidden. Although extensive efforts have been made,
existing face de-identification techniques are either insufficient in
photo-reality or incapable of well-balancing privacy and utility. In this
paper, we focus on tackling these challenges to improve face de-identification.
We propose IdentityDP, a face anonymization framework that combines a
data-driven deep neural network with a differential privacy (DP) mechanism.
This framework encompasses three stages: facial representations
disentanglement, $\epsilon$-IdentityDP perturbation and image reconstruction.
Our model can effectively obfuscate the identity-related information of faces,
preserve significant visual similarity, and generate high-quality images that
can be used for identity-agnostic computer vision tasks, such as detection,
tracking, etc. Different from the previous methods, we can adjust the balance
of privacy and utility through the privacy budget according to pratical demands
and provide a diversity of results without pre-annotations. Extensive
experiments demonstrate the effectiveness and generalization ability of our
proposed anonymization framework.
