Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2022

Abstract

Face de-identification (De-ID) removes face identity information in face images to avoid personal privacy leakage. Existing face De-ID breaks the raw identity by cutting out the face regions and recovering the corrupted regions via deep generators, which inevitably affect the generation quality and cannot control generation results according to subsequent intelligent tasks (e.g., facial expression recognition). In this work, for the first attempt, we think the face De-ID from the perspective of attribute editing and propose an attribute-aware anonymization network (A3GAN) by formulating face De-ID as a joint task of semantic suppression and controllable attribute injection. Intuitively, the semantic suppression removes the identity-sensitive information in embeddings while the controllable attribute injection automatically edits the raw face along the attributes that benefit De-ID. To this end, we first design a multi-scale semantic suppression network with a novel suppressive convolution unit (SCU), which can remove the face identity along multi-level deep features progressively. Then, we propose an attribute-aware injective network (AINet) that can generate De-ID-sensitive attributes in a controllable way (i.e., specifying which attributes can be changed and which cannot) and inject them into the latent code of the raw face. Moreover, to enable effective training, we design a new anonymization loss to let the injected attributes shift far away from the original ones. We perform comprehensive experiments on four datasets covering four different intelligent tasks including face verification, face detection, facial expression recognition, and fatigue detection, all of which demonstrate the superiority of our face De-ID over state-of-the-art methods.

Keywords

Face de-identification, Facial attribute, Controllability

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal, 2022 October 10-14

First Page

5303

Last Page

5313

ISBN

9781450392037

Identifier

10.1145/3503161.3547757

Publisher

ACM

City or Country

Lisbon, Portugal

Additional URL

http://doi.org/10.1145/3503161.3547757

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