mlsanityData to 7 Oct 2026

Reversing the Irreversible: A Survey on Inverse Biometrics

arXiv 2401.02861 ↗First posted 5 Jan 2024cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

With the widespread use of biometric recognition, several issues related to the privacy and security provided by this technology have been recently raised and analysed. As a result, the early common belief among the biometrics community of templates irreversibility has been proven wrong. It is now an accepted fact that it is possible to reconstruct from an unprotected template a synthetic sample that matches the bona fide one. This reverse engineering process, commonly referred to as textitinverse biometrics, constitutes a severe threat for biometric systems from two different angles: on the one hand, sensitive personal data (i.e., biometric data) can be derived from compromised unprotected templates; on the other hand, other powerful attacks can be launched building upon these reconstructed samples. Given its important implications, biometric stakeholders have produced over the last fifteen years numerous works analysing the different aspects related to inverse biometrics: development of reconstruction algorithms for different characteristics; proposal of methodologies to assess the vulnerabilities of biometric systems to the aforementioned algorithms; development of countermeasures to reduce the possible effects of attacks. The present article is an effort to condense all this information in one comprehensive review of: the problem itself, the evaluation of the problem, and the mitigation of the problem. The present article is an effort to condense all this information in one comprehensive review of: the problem itself, the evaluation of the problem, and the mitigation of the problem.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 140 references that are in the map. First posted on arXiv 5 Jan 2024.

Builds on

14 of 140 references.

References · 14

  1. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  4. 2016Pixel Recurrent Neural Networkscited by 814
  5. 2013Generating Sequences With Recurrent Neural Networkscited by 549
  6. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 459
  7. 2019GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstructioncited by 150
  8. 2017On the Reconstruction of Face Images from Deep Face Templatescited by 66
  9. 2016Inverting face embeddings with convolutional neural networkscited by 22
  10. 2016Do We Really Need to Collect Millions of Faces for Effective Face Recognition?cited by 17
  11. 2017Synthetic Iris Presentation Attack using iDCGANcited by 17
  12. 2017SREFI: Synthesis of Realistic Example Face Imagescited by 12
2 more
  1. 2023General Framework to Evaluate Unlinkability in Biometric Template Protection Systemscited by 9
  2. 2017DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolutioncited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.