Personalized Face Privacy Protection From a Single Image
arXiv cs.CV·Zachary Yahn, Fatih Ilhan, Tiansheng Huang, Selim Tekin, Sihao Hu, Yichang Xu, Margaret Loper, Ling Liu
17h agoQuick Take
FaceCloak provides personalized face privacy protection using a single image to generate effective cloaking masks.
Key Points
- Generates synthetic face images from one user photo.
- Enhances privacy by perturbing key facial regions.
- Proven effective across multiple datasets and recognition models.
📖 Reader Mode
~2 min readAbstract:Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via unauthorized use of facial recognition models. This paper presents FaceCloak, a novel personalized face privacy protection system, which can generate defensive identity-specific universal face privacy masks from a single image of a user, causing facial recognition to fail. FaceCloak introduces a three-stage personalized face perturbation learning methodology: (1) It generates a small set of high-variety synthetic face images of a person based on a single image of the person. (2) It learns face cloaking by adding more protection to key facial-identity leakage regions through iterative perturbation generation over the small set of synthetic images, effectively shifting a user's identity embedding towards a distant anchor identity and away from a similar one. (3) It generates a personalized identity-protective mask in the form of pixel-wise cloaking, which is light-weight and can be efficiently applied to any facial image of a user while maintaining good perceptual quality. Extensive experiments on three popular face datasets across ten recognition models show the effectiveness of FaceCloak compared to 29 other existing representative methods. Code is available at this https URL
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2605.19032 [cs.CV] |
| (or arXiv:2605.19032v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19032 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zachary Yahn [view email]
[v1]
Mon, 18 May 2026 18:56:56 UTC (4,858 KB)
— Originally published at arxiv.org
More from arXiv cs.CV
See more →arXiv cs.CV·Jinhao Jing, Zheng Ma, Jinwei Liang, Qiannian Zhao, Shawn Chen, Jing Yang, Por Lip Yee, Prayag Tiwari, Jingjing Bai, Benyou Wang, Lewei Lu, Zhan Su
1d ago FeaturedOriginal
GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning
AI Summary
GeoSym127K introduces a scalable neuro-symbolic framework for enhanced geometric reasoning in multimodal models.