Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2024
Abstract
Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G 2 Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility.
Keywords
Data privacy, Face recognition, Faces, Feature extraction, Generative adversarial networks, generative prior, geometric prior, identity-aware feature fusion, Information filtering, Information integrity, Reversible face anonymization
Discipline
Graphics and Human Computer Interfaces | Information Security
Research Areas
Cybersecurity
Publication
IEEE Transactions on Information Forensics and Security
Volume
19
First Page
8773
Last Page
8785
ISSN
1556-6013
Identifier
10.1109/TIFS.2024.3449104
Publisher
Institute of Electrical and Electronics Engineers
Citation
YANG, Haoxin; XU, Xuemiao; XU, Cheng; ZHANG, Huaidong; QIN, Jing; WANG, Yi; HENG, Pheng-Ann; and Shengfeng HE.
G2Face: High-Fidelity Reversible Face Anonymization via generative and geometric priors. (2024). IEEE Transactions on Information Forensics and Security. 19, 8773-8785.
Available at: https://ink.library.smu.edu.sg/sis_research/9273
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TIFS.2024.3449104