mlsanityData to 7 Oct 2026

Realistic Face Reconstruction from Facial Embeddings via Diffusion Models

arXiv 2602.13168 ↗First posted 13 Feb 2026cs.CV cs.LGcited by 0

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

With the advancement of face recognition (FR) systems, privacy-preserving face recognition (PPFR) systems have gained popularity for their accurate recognition, enhanced facial privacy protection, and robustness to various attacks. However, there are limited studies to further verify privacy risks by reconstructing realistic high-resolution face images from embeddings of these systems, especially for PPFR. In this work, we propose the face embedding mapping (FEM), a general framework that explores Kolmogorov-Arnold Network (KAN) for conducting the embedding-to-face attack by leveraging pre-trained Identity-Preserving diffusion model against state-of-the-art (SOTA) FR and PPFR systems. Based on extensive experiments, we verify that reconstructed faces can be used for accessing other real-word FR systems. Besides, the proposed method shows the robustness in reconstructing faces from the partial and protected face embeddings. Moreover, FEM can be utilized as a tool for evaluating safety of FR and PPFR systems in terms of privacy leakage. All images used in this work are from public datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 22 of its 42 references that are in the map. First posted on arXiv 13 Feb 2026.

Builds on

22 of 42 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection16 May

Other references · 21

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
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  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  7. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  8. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  9. 2024InstantID: Zero-shot Identity-Preserving Generation in Secondscited by 431
  10. 2018MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devicescited by 180
  11. 2017On the Reconstruction of Face Images from Deep Face Templatescited by 66
  12. 2019LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeupcited by 63
9 more
  1. 2022Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domaincited by 28
  2. 2024Privacy-Preserving Face Recognition Using Trainable Feature Subtractioncited by 24
  3. 2020Beyond Identity: What Information Is Stored in Biometric Face Templates?cited by 23
  4. 2017Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashingcited by 21
  5. 2023Privacy-Preserving Face Recognition Using Random Frequency Componentscited by 15
  6. 2021Towards Protecting Face Embeddings in Mobile Face Verification Scenarioscited by 14
  7. 2024Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Modelcited by 9
  8. 2024SlerpFace: Face Template Protection via Spherical Linear Interpolationcited by 8
  9. 2024Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domaincited by 2

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  3. 2023DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection
  4. 2025Secure and reversible face anonymization with diffusion models
  5. 2023Controllable Inversion of Black-Box Face Recognition Models via Diffusion
  6. 2024ID3: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition
  7. 2024Face Anonymization Made Simple
  8. 2023Towards Real-World Blind Face Restoration with Generative Diffusion Prior