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

arXiv: https://arxiv.org/abs/2209.04909 · HTML: https://mlsanity.com/paper/2209.04909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 11 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems](https://mlsanity.com/paper/2006.08376/) (2020)
- [Am I a Real or Fake Celebrity? Measuring Commercial Face Recognition Web APIs under Deepfake Impersonation Attack](https://mlsanity.com/paper/2103.00847/) (2021)
- [Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution](https://mlsanity.com/paper/2108.01077/) (2021)

## Abstract (arXiv)

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.