![[2510.01031] Secure and reversible face anonymization with diffusion models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2510.01031] Secure and reversible face anonymization with diffusion models
Summary
This paper presents a novel framework for secure and reversible face anonymization using diffusion models, addressing challenges in image quality and unauthorized de-anonymization.
Why It Matters
As concerns over privacy and data security grow, effective face anonymization techniques are crucial for protecting identity while maintaining the utility of visual data. This research advances the field by introducing a method that allows controlled identity recovery, enhancing both security and practical application in real-world scenarios.
Key Takeaways
- Introduces a diffusion-based framework for secure face anonymization.
- Enables reversible anonymization with secret-key conditioning.
- Demonstrates improved performance over previous methods in both anonymization and de-anonymization.
- Ensures robustness against incorrect or adversarial key attempts.
- Code will be publicly available, promoting further research and application.
Computer Science > Computer Vision and Pattern Recognition arXiv:2510.01031 (cs) [Submitted on 1 Oct 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Secure and reversible face anonymization with diffusion models Authors:Pol Labarbarie, Vincent Itier, William Puech View a PDF of the paper titled Secure and reversible face anonymization with diffusion models, by Pol Labarbarie and 1 other authors View PDF HTML (experimental) Abstract: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 s...
