Biometric Leakage from Generative Models and Adversarial Iris Swapping for Spoofing Eye-based Authentication

This thesis investigates the vulnerability of generative models trained on biometric data and explores digital spoofing attacks on iris-based authentication systems representative of AR/VR environments. We first explore how diffusion models trained on biometric data can memorize and leak iris images. Next, we evaluate the effectiveness of Cross-Attention GANs for iris-swapping attacks, demonstrating their ability to enable presentation attacks that spoof iris-recognition systems. Our experiments across several standard iris and VR datasets have an attack success rate of 100% within similar domains and generalize across domains with rates as high as 70%. Our findings highlight the need to consider vulnerabilities in biometric systems and strengthen defenses against digital presentation attacks produced by generative models.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Michalak, Jan Jakub

Chair dc:contributor.committeechair

• David-John, Brendan Matthew

Committee members dc:contributor.committeemember

• Ji, Bo
• Viswanath, Bimal

• Iris Authentication
• Digital Presentation Attacks
• Generative Adversarial Networks (GANs)

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:44229

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/135460