FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixing

• URL: http://arxiv.org/abs/2508.05636v1
• Date: Thu, 07 Aug 2025 17:59:59 GMT
• Title: FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixing
• Authors: Mohammed Talha Alam, Fahad Shamshad, Fakhri Karray, Karthik Nandakumar,
• Abstract summary: FaceAnonyMixer is a cancelable face generation framework that synthesizes privacy-preserving face images.<n>Experiments on benchmark datasets demonstrate that FaceAnonyMixer delivers superior recognition accuracy while providing significantly stronger privacy protection.
• Score: 7.067461105227436
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Advancements in face recognition (FR) technologies have amplified privacy concerns, necessitating methods that protect identity while maintaining recognition utility. Existing face anonymization methods typically focus on obscuring identity but fail to meet the requirements of biometric template protection, including revocability, unlinkability, and irreversibility. We propose FaceAnonyMixer, a cancelable face generation framework that leverages the latent space of a pre-trained generative model to synthesize privacy-preserving face images. The core idea of FaceAnonyMixer is to irreversibly mix the latent code of a real face image with a synthetic code derived from a revocable key. The mixed latent code is further refined through a carefully designed multi-objective loss to satisfy all cancelable biometric requirements. FaceAnonyMixer is capable of generating high-quality cancelable faces that can be directly matched using existing FR systems without requiring any modifications. Extensive experiments on benchmark datasets demonstrate that FaceAnonyMixer delivers superior recognition accuracy while providing significantly stronger privacy protection, achieving over an 11% gain on commercial API compared to recent cancelable biometric methods. Code is available at: https://github.com/talha-alam/faceanonymixer.

Related papers

• DiffUMI: Training-Free Universal Model Inversion via Unconditional Diffusion for Face Recognition [17.70133779192382]
  We introduce DiffUMI, a diffusion-based universal model inversion attack that requires no additional training.<n>It surpasses state-of-the-art attacks by 15.5% and 9.82% in success rate on standard and privacy-preserving face recognition systems, respectively.
  arXiv  Detail & Related papers  (2025-04-25T01:53:27Z)
• 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)
• G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors [71.69161292330504]
  Reversible face anonymization seeks to replace sensitive identity information in facial images with synthesized alternatives. This paper introduces Gtextsuperscript2Face, which leverages both generative and geometric priors to enhance identity manipulation. Our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility.
  arXiv  Detail & Related papers  (2024-08-18T12:36:47Z)
• Transferable Adversarial Facial Images for Privacy Protection [15.211743719312613]
  We present a novel face privacy protection scheme with improved transferability while maintain high visual quality. We first exploit global adversarial latent search to traverse the latent space of the generative model. We then introduce a key landmark regularization module to preserve the visual identity information.
  arXiv  Detail & Related papers  (2024-07-18T02:16:11Z)
• Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption [8.742970921484371]
  We propose a novel technique that combines Fully Homomorphic Encryption (FHE) with an existing template protection scheme known as PolyProtect. Our proposed approach ensures irreversibility and unlinkability, effectively preventing the leakage of soft biometric embeddings.
  arXiv  Detail & Related papers  (2024-04-24T23:56:03Z)
• Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification [55.741525129613535]
  "Disentangle Before Anonymize" is a novel two-stage Framework(DBAF)<n>This framework includes a Contrastive Identity Disentanglement (CID) module and a Key-authorized Reversible Identity Anonymization (KRIA) module.<n>Extensive experiments demonstrate that our method outperforms state-of-the-art de-identification approaches.
  arXiv  Detail & Related papers  (2023-11-15T08:59:02Z)
• 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.