Diversity and Novelty MasterPrints: Generating Multiple DeepMasterPrints for Increased User Coverage

TL;DR

This paper introduces Diversity and Novelty MasterPrints generated through evolutionary algorithms to improve user coverage in fingerprint spoofing, outperforming previous single solutions in coverage and generalization.

Contribution

It presents a novel approach using quality diversity evolutionary algorithms to generate multiple diverse DeepMasterPrints for enhanced user coverage.

Findings

Multi-print methods outperform single DeepMasterPrints in coverage

Generated prints increase user coverage significantly

Maintains high quality of fingerprint images

Abstract

This work expands on previous advancements in genetic fingerprint spoofing via the DeepMasterPrints and introduces Diversity and Novelty MasterPrints. This system uses quality diversity evolutionary algorithms to generate dictionaries of artificial prints with a focus on increasing coverage of users from the dataset. The Diversity MasterPrints focus on generating solution prints that match with users not covered by previously found prints, and the Novelty MasterPrints explicitly search for prints with more that are farther in user space than previous prints. Our multi-print search methodologies outperform the singular DeepMasterPrints in both coverage and generalization while maintaining quality of the fingerprint image output.