Researchers at New York University have developed a generative-adversary network for prototyping fingerprints. Images of these prints, called DeepMasterPrints, can be used as a key for hacking biometric identification systems.
The principle of creating DeepMasterPrints is to use two properties of fingerprints and biometric systems.
Many scanners do not read the entire print. They process a part of it, and then compare it with the exact same part of the print from the database. Thus, a fake print should correspond only to a part of the original one.
Secondly, the researchers noted that some features of prints are repeated. This means that an artificial footprint that contains a set of common features will correspond to several genuine footprints at once.
The study showed that at a system tolerance level of 0.1%, artificial fingerprints can forge up to 23% of all fingerprints from the database. An error of 1% allows neural networks to fake up to 77% of prints.
Experts compare the use of DeepMasterPrints with a brute-force attack, when an attacker sorts through all the passwords that may be appropriate in this case.