mlsanityData to 14 Oct 2026

Generative Adversarial Networks Applied for Privacy Preservation in Biometric-Based Authentication and Identification

arXiv 2509.20024 ↗First posted 24 Sep 2025cs.CV cs.AI cs.CRcited by 0

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Abstract (arXiv)

Biometric-based authentication systems are getting broadly adopted in many areas. However, these systems do not allow participating users to influence the way their data is used. Furthermore, the data may leak and can be misused without the users' knowledge. In this paper, we propose a new authentication method that preserves the privacy of individuals and is based on a generative adversarial network (GAN). Concretely, we suggest using the GAN for translating images of faces to a visually private domain (e.g., flowers or shoes). Classifiers, which are used for authentication purposes, are then trained on the images from the visually private domain. Based on our experiments, the method is robust against attacks and still provides meaningful utility.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 32 references that are in the map. First posted on arXiv 24 Sep 2025.

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22 of 32 references.

References · 22

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  2. 2016Improved Techniques for Training GANscited by 2,970
  3. 2017Improved Training of Wasserstein GANscited by 2,235
  4. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  6. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,098
  7. 2016Least Squares Generative Adversarial Networkscited by 1,001
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
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  10. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  11. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  12. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
10 more
  1. 2019U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translationcited by 186
  2. 2018Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mappingcited by 113
  3. 2018Learning to Anonymize Faces for Privacy Preserving Action Detectioncited by 63
  4. 2016Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Imagescited by 45
  5. 2019SPA-GAN: Spatial Attention GAN for Image-to-Image Translationcited by 43
  6. 2019TraVeLGAN: Image-to-image Translation by Transformation Vector Learningcited by 35
  7. 2020A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networkscited by 27
  8. 2019Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detectioncited by 26
  9. 2020More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligencecited by 23
  10. 2020Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluationcited by 8

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