Machine learning masters the fingerprint to fool biometric systems: Synthetic fingerprints can spoof smartphone fingerprint sensors

Much the way that a master key can unlock every door in a building, these "DeepMasterPrints" use artificial intelligence to match a large number of prints stored in fingerprint databases and could thus theoretically unlock a large number of devices. The research team was headed by NYU Tandon Associate Professor of Computer Science and Engineering Julian Togelius and doctoral student Philip Bontrager, the lead author of the paper, who presented it at the IEEE International Conference of Biometrics: Theory, Applications and Systems, where it won the Best Paper Award. The work builds on earlier research led by Nasir Memon, professor of computer science and engineering and associate dean for online learning at NYU Tandon. Memon, who coined the term "MasterPrint," described how fingerprint-based systems use partial fingerprints, rather than full ones, to confirm identity. Devices typically allow users to enroll several different finger images, and a match for any saved partial print is enough to confirm identity.