fingerprint recognition on smartphone

Fingerprint recognition could be vulnerable to AI attack

Published: 18:36, December 9, 2018

Device manufacturers need to get smarter about security systems that rely on fingerprint recognition. The systems could be vulnerable to attack by artificial intelligence (AI), say researchers.

A team from New York University (NYU) School of Engineering in Brooklyn, NY created fake fingerprints that act like “master keys.”

fingerprint recognition on smartphoneWorldwide, millions of devices such as smartphones use fingerprint recognition for user authentication. Image: Pixabay.com

They trained an AI system capable of “machine learning” and with access to fingerprint libraries.

Such a system, they say, could potentially penetrate devices that use “touch-based authentication.”

Millions of consumer devices use fingerprint recognition

Consumers tend to trust systems that have fingerprint recognition. Worldwide, millions of devices such as smartphones use this form of “biometric authentication.”

The NYU study, however, reveals that these systems could be “surprisingly vulnerable” to security attacks by AI.

One common feature that makes them especially vulnerable is that they use a partial fingerprint for authentication.

To set up fingerprint recognition, devices typically ask users for several different finger images. The authentication system only needs to match any saved partial print to confirm identity.

Partial fingerprint recognition easier to fool

It is much easier to fool a system that uses partial fingerprints because they are less likely to be unique compared with full fingerprints.

Previous research has shown that partial prints have features in common.

An AI system with machine learning and access to sufficient samples can learn what these features are and create what the NYU team calls “MasterPrints.”

“MasterPrints,” they explain, “are real or synthetic fingerprints that can fortuitously match with a large number of fingerprints.”

New AI has much higher ‘attack accuracy’

The new study describes a more accurate system that the team calls “DeepMasterPrints.”

To create fake fingerprints using the MasterPrints method, the AI system “learns” from data.

The AI for DeepMasterPrints, on the other hand, learns from images. This takes the “attack accuracy” to another level.

The team ran a series of experiments in which they showed how their DeepMasterPrints could potentially unlock a touch-based authentication system for up to 1 in 5 users.

‘Wake-up call for device manufacturers’

“Fingerprint-based authentication,” says lead study author Philip Bontrager, a doctoral student at NYU, “is still a strong way to protect a device or a system, but at this point, most systems don’t verify whether a fingerprint or other biometric is coming from a real person or a replica.”

The results of their experiments, he adds, “should be a wake-up call for device manufacturers about the potential for artificial fingerprint attacks.”

He and his colleagues suggest that manufacturers should be thinking about using “multifactor authentication.”

What is machine learning?

Machine learning is a way of training computers to make predictions that have a much higher chance of success than just generating random choices.

First, engineers train computer algorithms by giving them lots of examples. Then the algorithms make a prediction based on what they have “learned” during the training phase.

Amazon, for instance, are using machine learning to improve online browsing for shoppers.

The aim of the service is to help shoppers to find the products they want much more quickly. It asks them to vote on a series of images and then recommends some products.

First, it shows the customer an image of the type of product they have keyed in – for example “table” – and asks them if they like it or not (they select thumb up or down).

Which image appears next depends on whether the customer liked the previous image or not. Again, the customer selects either thumb up or thumb down.

After a series of images, the machine learning algorithm extracts the common attributes of all those that the customer liked.

It then scans all the products in the database, finds those that most closely match the cluster of attributes, and presents the selection to the customer.

AI (artificial intelligence) refers to software technology that makes machines behave and think like human beings.


Catharine Paddock PhD Avatar

Other News

Cocoa study finds monitoring change cut estimated false reporting from 25% to 11%

Aug 14, 2026

Moody’s methodology change led affected companies to borrow more, study finds

Aug 14, 2026

Pony.ai and Uber plan more than 2,000 robotaxis across five European cities

Aug 14, 2026

Great Britain temporarily halts disposable barbecue sales over wildfire risk

Aug 14, 2026

SpaceX completes all-stock Cursor acquisition at $60 billion implied value

Aug 14, 2026

Good company news does not necessarily mean higher returns, study finds

Aug 14, 2026

Maersk raises 2026 forecast as freight rates and volumes lift Q2 earnings

Aug 14, 2026

Shoppers who used smart trolley screens spent 32% more, study finds

Aug 13, 2026

Stressful drives to work linked to negative behavior towards colleagues

Aug 13, 2026

Extra payments on oldest loan may cost borrowers more, study finds

Aug 13, 2026

Cisco revenue rises 18% as hyperscaler AI orders reach $9.3 billion

Aug 12, 2026

Bank of America agrees to invest up to $1.9 billion in Jio Credit

Aug 12, 2026

Your salary went up. So why do you feel poorer?

Aug 12, 2026

Fitch keeps India at BBB- as high debt offsets strong growth

Aug 11, 2026

China’s “handcrafted economy” shows how AI could expand one-person businesses

Aug 11, 2026

Joby Aviation agrees $500 million Resonant Sciences deal to expand defense business

Aug 11, 2026

Sony and TSMC agree $4.7 billion capital plan for image-sensor joint venture

Aug 11, 2026

Obesity linked to lower employment and reduced work performance in European review

Aug 11, 2026

Archer to acquire Boeing’s Wisk, Insitu and SkyGrid businesses

Aug 10, 2026

CECO orders nearly triple as power projects push backlog above $1.8 billion

Aug 10, 2026