# Password-conditioned Anonymization and Deanonymization with Face Identity Transformers

arXiv: https://arxiv.org/abs/1911.11759 · HTML: https://mlsanity.com/paper/1911.11759/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 24 of 24)

- [Diff-Privacy: Diffusion-based Face Privacy Protection](https://mlsanity.com/paper/2309.05330/) (2023)
- [RiDDLE: Reversible and Diversified De-identification with Latent Encryptor](https://mlsanity.com/paper/2303.05171/) (2023)
- [G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors](https://mlsanity.com/paper/2408.09458/) (2024)
- [Face Anonymization Made Simple](https://mlsanity.com/paper/2411.00762/) (2024)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [On Generating Identifiable Virtual Faces](https://mlsanity.com/paper/2110.07986/) (2021)
- [Disguise without Disruption: Utility-Preserving Face De-Identification](https://mlsanity.com/paper/2303.13269/) (2023)
- [Anonymization for Skeleton Action Recognition](https://mlsanity.com/paper/2111.15129/) (2021)
- [Privacy-preserving Optics for Enhancing Protection in Face De-identification](https://mlsanity.com/paper/2404.00777/) (2024)
- [Medical Manifestation-Aware De-Identification](https://mlsanity.com/paper/2412.10804/) (2024)
- [NullFace: Training-Free Localized Face Anonymization](https://mlsanity.com/paper/2503.08478/) (2025)
- [Big Brother is Watching: Proactive Deepfake Detection via Learnable Hidden Face](https://mlsanity.com/paper/2504.11309/) (2025)
- [Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification](https://mlsanity.com/paper/2507.21703/) (2025)
- [Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection](https://mlsanity.com/paper/2307.00481/) (2023)
- [A Key-Driven Framework for Identity-Preserving Face Anonymization](https://mlsanity.com/paper/2409.03434/) (2024)
- [SemDP: Semantic-level Differential Privacy Protection for Face Datasets](https://mlsanity.com/paper/2412.15590/) (2024)
- [ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer](https://mlsanity.com/paper/2510.11073/) (2025)
- [PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow](https://mlsanity.com/paper/2307.09146/) (2023)
- [Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification](https://mlsanity.com/paper/2311.08786/) (2023)
- [VerA: Versatile Anonymization Applicable to Clinical Facial Photographs](https://mlsanity.com/paper/2312.02124/) (2023)
- [Computation-Efficient and Recognition-Friendly 3D Point Cloud Privacy Protection](https://mlsanity.com/paper/2503.15818/) (2025)
- [Secure and reversible face anonymization with diffusion models](https://mlsanity.com/paper/2510.01031/) (2025)
- [SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacy](https://mlsanity.com/paper/2602.04994/) (2026)
- [HYDRO: Towards Non-Reversible Face De-Identification Using a High-Fidelity Hybrid Diffusion and Target-Oriented Approach](https://mlsanity.com/paper/2609.27011/) (2026)

## Abstract (arXiv)

Cameras are prevalent in our daily lives, and enable many useful systems built upon computer vision technologies such as smart cameras and home robots for service applications. However, there is also an increasing societal concern as the captured images/videos may contain privacy-sensitive information (e.g., face identity). We propose a novel face identity transformer which enables automated photo-realistic password-based anonymization as well as deanonymization of human faces appearing in visual data. Our face identity transformer is trained to (1) remove face identity information after anonymization, (2) make the recovery of the original face possible when given the correct password, and (3) return a wrong--but photo-realistic--face given a wrong password. Extensive experiments show that our approach enables multimodal password-conditioned face anonymizations and deanonymizations, without sacrificing privacy compared to existing anonymization approaches.