Fingerprint Membership and Identity Inference Against Generative Adversarial Networks

TL;DR

This paper investigates the privacy vulnerabilities of fingerprint generative models, demonstrating that identity inference attacks can effectively compromise biometric data protected by generative adversarial networks.

Contribution

It introduces and evaluates an identity inference attack on fingerprint GANs, highlighting privacy risks and extending applicability to other biometric data.

Findings

The attack successfully infers identities from GAN-generated fingerprint data.

The method is effective across various configurations.

It can be adapted to other biometric modalities.

Abstract

Generative models are gaining significant attention as potential catalysts for a novel industrial revolution. Since automated sample generation can be useful to solve privacy and data scarcity issues that usually affect learned biometric models, such technologies became widely spread in this field. In this paper, we assess the vulnerabilities of generative machine learning models concerning identity protection by designing and testing an identity inference attack on fingerprint datasets created by means of a generative adversarial network. Experimental results show that the proposed solution proves to be effective under different configurations and easily extendable to other biometric measurements.