Personalized Face Privacy Protection From a Single Image

• URL: http://arxiv.org/abs/2605.19032v1
• Date: Mon, 18 May 2026 18:56:56 GMT
• Title: Personalized Face Privacy Protection From a Single Image
• Abstract summary: Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources.<n>This paper presents FaceCloak, a novel personalized face privacy protection system.<n>It can generate defensive identity-specific universal face privacy masks from a single image of a user, causing facial recognition to fail.
• Score: 18.118718063673423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 https://github.com/zacharyyahn/FaceCloak

Related papers

• Protego: User-Centric Pose-Invariant Privacy Protection Against Face Recognition-Induced Digital Footprint Exposure [4.752324012811179]
  Services like Clearview AI and PimEyes allow anyone to upload a facial photo and retrieve a large amount of online content associated with that person.<n>This not only enables identity inference but also exposes their digital footprint, such as social media activity, private photos, and news reports, often without their consent.<n>We propose Protego, a user-centric privacy protection method that safeguards facial images from such retrieval-based privacy intrusions.
  arXiv  Detail & Related papers  (2025-08-04T04:03:01Z)
• iFADIT: Invertible Face Anonymization via Disentangled Identity Transform [51.123936665445356]
  Face anonymization aims to conceal the visual identity of a face to safeguard the individual's privacy.<n>This paper proposes a novel framework named iFADIT, an acronym for Invertible Face Anonymization via Disentangled Identity Transform.
  arXiv  Detail & Related papers  (2025-01-08T10:08:09Z)
• Personalized Privacy Protection Mask Against Unauthorized Facial Recognition [11.078375461923088]
  Face recognition (FR) can be abused for privacy intrusion. Governments, private companies, or even individual attackers can collect facial images by web scraping to build an FR system identifying human faces without their consent. This paper introduces Chameleon, which learns to generate a user-centric personalized privacy protection mask, coined as P3-Mask, to protect facial images against unauthorized FR with three salient features.
  arXiv  Detail & Related papers  (2024-07-19T01:59:00Z)
• FlashFace: Human Image Personalization with High-fidelity Identity Preservation [59.76645602354481]
  FlashFace allows users to easily personalize their own photos by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customization methods by higher-fidelity identity preservation and better instruction following.
  arXiv  Detail & Related papers  (2024-03-25T17:59:57Z)
• Privacy-Preserving Face Recognition Using Trainable Feature Subtraction [40.47645421424354]
  Face recognition has led to increasing privacy concerns. This paper explores face image protection against viewing and recovery attacks. We distill our methodologies into a novel privacy-preserving face recognition method, MinusFace.
  arXiv  Detail & Related papers  (2024-03-19T05:27:52Z)
• PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow [69.78820726573935]
  We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model. In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one.
  arXiv  Detail & Related papers  (2023-07-18T10:55:54Z)
• CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search [10.16904417057085]
  Deep learning based face recognition systems can enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate naturalistic images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model.
  arXiv  Detail & Related papers  (2023-06-16T17:58:15Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders [81.21440457805932]
  We propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. randomly masked face images are used to train the reconstruction module in FaceMAE. We also perform sufficient privacy-preserving face recognition on several public face datasets.
  arXiv  Detail & Related papers  (2022-05-23T07:19:42Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.