# Reversing the Irreversible: A Survey on Inverse Biometrics

arXiv: https://arxiv.org/abs/2401.02861 · HTML: https://mlsanity.com/paper/2401.02861/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/1902.05978/) (2019)
- [On the Reconstruction of Face Images from Deep Face Templates](https://mlsanity.com/paper/1703.00832/) (2017)
- [Inverting face embeddings with convolutional neural networks](https://mlsanity.com/paper/1606.04189/) (2016)
- [Do We Really Need to Collect Millions of Faces for Effective Face Recognition?](https://mlsanity.com/paper/1603.07057/) (2016)
- [Synthetic Iris Presentation Attack using iDCGAN](https://mlsanity.com/paper/1710.10565/) (2017)
- [SREFI: Synthesis of Realistic Example Face Images](https://mlsanity.com/paper/1704.06693/) (2017)
- [General Framework to Evaluate Unlinkability in Biometric Template Protection Systems](https://mlsanity.com/paper/2311.04633/) (2023)
- [DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution](https://mlsanity.com/paper/1705.07386/) (2017)

## Built on by (most cited first, 4 of 4)

- [CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition](https://mlsanity.com/paper/2409.14774/) (2024)
- [Cancelable Biometric Template Generation Using Random Feature Vector Transformations](https://mlsanity.com/paper/2503.15648/) (2025)
- [Beyond Detection: Multi-Scale Hidden-Code for Natural Image Deepfake Recovery and Factual Retrieval](https://mlsanity.com/paper/2602.22759/) (2026)
- [Scalable Secure Biometric Authentication without Auxiliary Identifiers](https://mlsanity.com/paper/2604.25071/) (2026)

## 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.