• Home
  • Cyber Crime
  • Cyber warfare
  • APT
  • Data Breach
  • Deep Web
  • Digital ID
  • Hacking
  • Hacktivism
  • Intelligence
  • Internet of Things
  • Laws and regulations
  • Malware
  • Mobile
  • Reports
  • Security
  • Social Networks
  • Terrorism
  • ICS-SCADA
  • POLICIES
  • Contact me
MUST READ

Russia-linked APT28 use Signal chats to target Ukraine official with malware

 | 

China-linked APT Salt Typhoon targets Canadian Telecom companies

 | 

U.S. warns of incoming cyber threats following Iran airstrikes

 | 

McLaren Health Care data breach impacted over 743,000 people

 | 

American steel giant Nucor confirms data breach in May attack

 | 

The financial impact of Marks & Spencer and Co-op cyberattacks could reach £440M

 | 

Iran-Linked Threat Actors Cyber Fattah Leak Visitors and Athletes' Data from Saudi Games

 | 

SECURITY AFFAIRS MALWARE NEWSLETTER ROUND 50

 | 

Security Affairs newsletter Round 529 by Pierluigi Paganini – INTERNATIONAL EDITION

 | 

Iran confirmed it shut down internet to protect the country against cyberattacks

 | 

Godfather Android trojan uses virtualization to hijack banking and crypto apps

 | 

Cloudflare blocked record-breaking 7.3 Tbps DDoS attack against a hosting provider

 | 

Linux flaws chain allows Root access across major distributions

 | 

A ransomware attack pushed the German napkin firm Fasana into insolvency

 | 

Researchers discovered the largest data breach ever, exposing 16 billion login credentials

 | 

China-linked group Salt Typhoon breached satellite firm Viasat

 | 

Iran experienced a near-total national internet blackout

 | 

Malicious Minecraft mods distributed by the Stargazers DaaS target Minecraft gamers

 | 

Healthcare services company Episource data breach impacts 5.4 Million people

 | 

Watch out, Veeam fixed a new critical bug in Backup & Replication product

 | 
  • Home
  • Cyber Crime
  • Cyber warfare
  • APT
  • Data Breach
  • Deep Web
  • Digital ID
  • Hacking
  • Hacktivism
  • Intelligence
  • Internet of Things
  • Laws and regulations
  • Malware
  • Mobile
  • Reports
  • Security
  • Social Networks
  • Terrorism
  • ICS-SCADA
  • POLICIES
  • Contact me
  • Home
  • Breaking News
  • Hacking
  • Security
  • From 2018: DeepMasterPrints: deceive fingerprint recognition systems with MasterPrints generated with GANs

From 2018: DeepMasterPrints: deceive fingerprint recognition systems with MasterPrints generated with GANs

Pierluigi Paganini August 18, 2024

Boffins demonstrated the vulnerability of fingerprint recognition systems to dictionary attacks using ‘MasterPrints, ‘which are fingerprints that can match multiple other prints.

A team of researchers from US universities demonstrated how to deceive fingerprint recognition systems through dictionary attacks using ‘MasterPrints,’ which are fingerprints that can match multiple other prints.

The experts introduced DeepMasterPrints, which are complete image-level prints, and used a method called Latent Variable Evolution, involving a Generative Adversarial Network (GAN) trained on real fingerprint images and a search strategy to generate prints that maximize impostor matches.

Fingerprint recognition is increasingly used in various applications, but small-sized sensors, like those on smartphones, only capture partial fingerprints, making them more susceptible to incorrect matches. The researchers pointed out that their study is the first to create image-level synthetic “MasterPrints,” which can exploit this vulnerability. The research demonstrates that these “DeepMasterPrints” can spoof 23% of subjects at a 0.1% false match rate, and up to 77% at a 1% false match rate. This highlights the significant security risks posed by using small, low-resolution fingerprint sensors.

The researchers trained two generator networks using the Wasserstein GAN (WGAN) algorithm to create synthetic fingerprints. One network was trained on fingerprints from a capacitive sensor, and the other on inked and rolled fingerprints. Both networks used a deep convolutional GAN architecture and were trained adversarially with a Wasserstein loss function and RMSProp optimizer at a learning rate of 0.00005. Each generator was trained for 120,000 updates, with the discriminator trained five times for each generator update. To prevent blocky artifacts, the researchers switched from deconvolutions to using upsampling with convolutions.

To create a DeepMasterPrint, the researchers needed to optimize the latent variables (inputs) for the generator network. These variables exist in a complex, 100-dimensional space, and the goal was to find the best values (or “points”) in this space that would produce the most effective fingerprint images. The Latent Variable Evolution (LVE) technique samples these points, converts them into images, and scores them based on how many identities they can match. The researchers used an evolutionary algorithm, e.g. CMA-ES, to navigate the challenging optimization task because the process lacks clear gradients. They spent three days per fingerprint in the optimization process, evaluating each one against multiple fingerprint matchers, including the widely used VeriFinger, Bozorth3, and Innovatrics systems. The key weakness exploited by DeepMasterPrints is that a match is considered valid if even one of 12 partial fingerprints matches, making the system vulnerable.

The experts tested DeepMasterPrint attacks mainly against smartphones because of their small fingerprint sensors. Since smartphones use capacitive sensors, the researchers created their DeepMasterPrints using a capacitive fingerprint dataset and evaluated them with the VeriFinger matcher.

“In our work, we created a DeepMasterPrint that is intended to spoof an arbitrary identity in a single try. Previous work had much worse results when given only a single attempt. Besides providing an image, LVE creates a much more effective MasterPrint.” concludes the researchers. “Table 3 has the results of the minutiae-only approaches and the capacitive DeepMasterPrint image [23]. In the previous work by Roy et al. [25], the authors generated a suite of five fingerprint templates that were used sequentially to launch an attack, assuming five attempts. Our results for a single DeepMasterPrint is comparable to this suite of multiple MasterPrints. We expect LVE to do very well in creating sequential DeepMasterPrints.”

The study conducted by the researchers demonstrated that using LVE to find latent variables is possible to produce images matching a large number of fingerprints. The method successfully creates full fingerprint images, which could be used in real attacks. The technique is effective across different fingerprint matchers and datasets and has potential applications in both security and computational creativity research.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, MasterPrints)


facebook linkedin twitter

DeepMasterPrints fingerprint recognition systems GAN Hacking hacking news information security news IT Information Security MasterPrints Pierluigi Paganini Security Affairs Security News

you might also like

Pierluigi Paganini June 24, 2025
Russia-linked APT28 use Signal chats to target Ukraine official with malware
Read more
Pierluigi Paganini June 24, 2025
China-linked APT Salt Typhoon targets Canadian Telecom companies
Read more

leave a comment

newsletter

Subscribe to my email list and stay
up-to-date!

    recent articles

    Russia-linked APT28 use Signal chats to target Ukraine official with malware

    APT / June 24, 2025

    China-linked APT Salt Typhoon targets Canadian Telecom companies

    APT / June 24, 2025

    U.S. warns of incoming cyber threats following Iran airstrikes

    Cyber warfare / June 24, 2025

    McLaren Health Care data breach impacted over 743,000 people

    Data Breach / June 23, 2025

    American steel giant Nucor confirms data breach in May attack

    Data Breach / June 23, 2025

    To contact me write an email to:

    Pierluigi Paganini :
    pierluigi.paganini@securityaffairs.co

    LEARN MORE

    QUICK LINKS

    • Home
    • Cyber Crime
    • Cyber warfare
    • APT
    • Data Breach
    • Deep Web
    • Digital ID
    • Hacking
    • Hacktivism
    • Intelligence
    • Internet of Things
    • Laws and regulations
    • Malware
    • Mobile
    • Reports
    • Security
    • Social Networks
    • Terrorism
    • ICS-SCADA
    • POLICIES
    • Contact me

    Copyright@securityaffairs 2024

    We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
    Cookie SettingsAccept All
    Manage consent

    Privacy Overview

    This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities...
    Necessary
    Always Enabled
    Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
    Non-necessary
    Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
    SAVE & ACCEPT