G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors
<|reference_start|>G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors: 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\textsuperscript{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. Code is available at https://github.com/Harxis/G2Face.<|reference_end|>
@article{yang2024g2face:,
    title={G2Face: High-Fidelity Reversible Face Anonymization via Generative and
  Geometric Priors},
    author={Haoxin Yang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Jing Qin, Yi Wang,
  Pheng-Ann Heng, Shengfeng He},
    journal={arXiv preprint arXiv:2408.09458},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09458},
    primaryClass={cs.CV}
}