000			01425nam a2200169 a 4500
005			20260901030330.0
008			250101s2020 xx o 000 0 eng d
100	1		_aDanny Keller
245	1	0	_aFuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning
264		1	_barXiv _c2020
336			_atext
338			_aonline resource
520			_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.
506	0		_aOpen access — freely available to read.
856	4	0	_uhttps://arxiv.org/pdf/2012.13293v1 _yRead the full paper (PDF)
942			_cERES
999			_c644 _d644