01580nam a2200181 a 450000500170000000800400001710000170005724501010007426400160017533600090019133800200020052009010022050600460112185600660116794200090123399900130124295201430125520260901030330.0250101s2020    xx     o     000 0 eng d1 aDanny Keller10aFuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning 1barXivc2020  atext  aonline resource  aIn this work, we study the protection that fuzzy commitments offer when they are applied to facial images, processed by the state of the art deep learning facial recognition systems. We show that while these systems are capable of producing great accuracy, they produce templates of too little entropy. As a result, we present a reconstruction attack that takes a protected template, and reconstructs a facial image. The reconstructed facial images greatly resemble the original ones. In the simplest attack scenario, more than 78% of these reconstructed templates succeed in unlocking an account (when the system is configured to 0.1% FAR). Even in the "hardest" settings (in which we take a reconstructed image from one system and use it in a different system, with different feature extraction process) the reconstructed image offers 50 to 120 times higher success rates than the system's FAR.0 aOpen access — freely available to read.40uhttps://arxiv.org/pdf/2012.13293v1yRead the full paper (PDF)  cERES  c644d644  001040738CYBaMAINbMAINcSCICOMPd2026-09-01l0pYGE000905r2026-09-01 03:03:30uhttps://arxiv.org/pdf/2012.13293v1w2026-09-01yPAPER