Reversing the Irreversible: A Survey on Inverse Biometrics
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
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Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024CFVNet: An End-to-End Cancelable Finger Vein Network for Recognitioncited by 3
- 2025Cancelable Biometric Template Generation Using Random Feature Vector Transformationscited by 0
- 2026Beyond Detection: Multi-Scale Hidden-Code for Natural Image Deepfake Recovery and Factual Retrievalcited by 0
- 2026Scalable Secure Biometric Authentication without Auxiliary Identifierscited by 0
- 2026Scalable Secure Biometric Authentication without Auxiliary Identifiers27 Apr 2026
- 2026Beyond Detection: Multi-Scale Hidden-Code for Natural Image Deepfake Recovery and Factual Retrieval26 Feb 2026
- 2025Cancelable Biometric Template Generation Using Random Feature Vector Transformations19 Mar 2025
- 2024CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition23 Sep 2024