Secure and reversible face anonymization with diffusion models
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918357866905600 |
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| author | Labarbarie, Pol Itier, Vincent Puech, William |
| author_facet | Labarbarie, Pol Itier, Vincent Puech, William |
| contents | Face anonymization aims to protect sensitive identity information by altering faces while preserving visual realism and utility for downstream computer vision tasks. Current methods struggle to simultaneously ensure high image quality, strong security guarantees, and controlled reversibility for authorized identity recovery at a later time. To improve the image quality of generated anonymized faces, recent methods have adopted diffusion models. However, these new diffusion-based anonymization methods do not provide a mechanism to restrict de-anonymization to trusted parties, limiting their real-world applicability. In this paper, we present the first diffusion-based framework for secure, reversible face anonymization via secret-key conditioning. Our method injects the secret key directly into the diffusion process, enabling anonymization and authorized face reconstruction while preventing unauthorized de-anonymization. The use of deterministic forward and reverse diffusion steps guarantees exact identity recovery when the correct secret key is available. Experiments on CelebA-HQ and LFW demonstrate that our approach achieves better anonymization and de-anonymization capabilities than prior work. We also show that our method remains robust to incorrect or adversarial key de-anonymization. Our code will be made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Secure and reversible face anonymization with diffusion models Labarbarie, Pol Itier, Vincent Puech, William Computer Vision and Pattern Recognition Machine Learning Face anonymization aims to protect sensitive identity information by altering faces while preserving visual realism and utility for downstream computer vision tasks. Current methods struggle to simultaneously ensure high image quality, strong security guarantees, and controlled reversibility for authorized identity recovery at a later time. To improve the image quality of generated anonymized faces, recent methods have adopted diffusion models. However, these new diffusion-based anonymization methods do not provide a mechanism to restrict de-anonymization to trusted parties, limiting their real-world applicability. In this paper, we present the first diffusion-based framework for secure, reversible face anonymization via secret-key conditioning. Our method injects the secret key directly into the diffusion process, enabling anonymization and authorized face reconstruction while preventing unauthorized de-anonymization. The use of deterministic forward and reverse diffusion steps guarantees exact identity recovery when the correct secret key is available. Experiments on CelebA-HQ and LFW demonstrate that our approach achieves better anonymization and de-anonymization capabilities than prior work. We also show that our method remains robust to incorrect or adversarial key de-anonymization. Our code will be made publicly available. |
| title | Secure and reversible face anonymization with diffusion models |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2510.01031 |