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Nylon masks can threaten your digital security
“Many recognition systems would struggle with this,” says professor.
Masks are being used to imitate other people's identities.
Professor and cybersecurity expert Raghavendra Ramachandra at NTNU has spent many years investigating how best to secure digital systems designed to prevent unauthorised access to your mobile phone or online bank account.
He has conducted this research together with colleagues from NTNU and several universities around the world.
“Many of these systems are now based on biometrics. This means that we no longer need to remember passwords or carry around as many keys and access tokens as we used to. Instead, these systems secure our devices using our own unique biological characteristics,” says Ramachandra.
It is possible to imitate identifying characteristics
Biometrics can include anything from your face and fingerprints to your eyes.
Although such systems are in many ways reliable, convenient, and user-friendly, they are also vulnerable to certain types of attack.
“There are good reasons why biometric security systems are used today by banks, at passport controls, and in many other areas where security is paramount. At the same time, these systems have proved vulnerable to what we call presentation attacks,” says Ramachandra.
A presentation attack involves an attacker presenting a biometric system with a copy of the biological characteristics that the system is designed to recognise.
This could, for example, be a mask.
Systems that recognise a face
“Many newer phones, for example, have built-in facial recognition. Imagine that someone wants to gain access to my phone. They could go to my Facebook profile, download my profile picture, and print it out on a sheet of paper. If they then present my phone with that piece of paper in an attempt to gain unauthorised access, they are carrying out a presentation attack,” Ramachandra explains.
Whether the attack actually succeeds depends entirely on how sophisticated both the attack method and the recognition system are.
Most modern facial-recognition systems would probably not be fooled by a paper printout.
“But let’s say that someone who follows me on social media gets hold of several selfies of me taken from different angles. That could provide the basis for creating a 3D model of my face. And if you have a 3D model of my face, you can carry out sophisticated presentation attacks,” the researcher explains.
Very high attack potential
Ramachandra picks up a lifelike silicone mask. The imitation face has realistic depth and exactly the right proportions between the nose, eyes, and mouth.
Because it is made from silicone, even the texture of the skin can be reproduced with a high degree of realism.
“This means that we consider such masks to have very high attack potential. In other words, there is a strong chance that they will succeed in fooling a biometric system,” says Ramachandra. “Many recognition systems would struggle with this."
The mask is so realistic that you might wonder how it is even possible to secure a system against something like this.
“One way of solving the problem is to use two recognition systems in parallel. These masks may be able to fool a purely facial recognition system. But if the system also uses voice recognition, you won’t be granted access,” says the researcher.
The drawback is that this quickly makes things much more cumbersome for users: not only do they have to show their face, they also have to use their voice.
“Our goal is therefore to create one system capable of handling several different types of attack on its own, including new methods that have not previously been encountered,” says Ramachandra.
An old favourite in a new guise
The barrier to using silicone masks is high. They are expensive and difficult to make.
“Because everyone has a different head shape, these masks have to be custom-made for the person who will wear them so that the proportions are not distorted when the mask is put on. That quickly makes the process expensive and cumbersome,” the cybersecurity expert explains.
That is not the case with nylon masks.
“The nylon masks we used in this research can easily be purchased online. A high-resolution image of the person being impersonated is printed directly onto the surface in a way that preserves even the smallest facial features when the material is stretched,” says Ramachandra.
Nylon masks may not be as realistic as silicone masks, but experiments carried out by Ramachandra and his colleagues nevertheless show that they have relatively high attack potential and can fool advanced biometric systems.
The fact that these masks can be ordered easily and cheaply online also demonstrates just how feasible such attacks have become in real-world situations, according to the researcher.
Skin, eyes, and micro-movements
Nylon masks are just one of the latest tools in the ever-expanding toolbox used by cybercriminals to gain access to our digital devices.
“It's a constant game of cat and mouse to keep up. As soon as we find ways of detecting one attack method, another soon appears,” says Ramachandra.
As mentioned, the researchers’ goal is therefore to develop a system capable of detecting several attack methods at once – including new and previously unknown ones.
They have already come a long way towards achieving this.
“Using machine-learning algorithms trained on things such as skin texture, eye movements, and so-called micro-movements, we have developed a tool that can detect these attack methods and also performs well against new ones,” explains Ramachandra.
Micro-movements are tiny movements that all humans make constantly without being aware of them. They occur, for example, when we breathe, swallow, or unconsciously raise an eyebrow.
“This tool can be integrated into mobile phones, online banking services, and other access-control systems. Our experiments show that the system can also detect types of attack it has not previously been trained on,” he says.
If the face does not behave the way a real face is expected to behave, it is identified as an attempted attack.
Ramachandra and his colleagues are now also investigating whether it is possible to identify the person behind the mask. Preliminary experiments suggest this may be possible using a multispectral camera. This is a camera that captures images in several parts of the light spectrum, including wavelengths of light that we cannot see.
Reference:
Anand et al. A Systematic Failure Analysis of Vision Foundation Models for Open Set Iris Presentation Attack Detection (Abstract), IEEE Transactions on Biometrics, Behavior, and Identity Science, 2026. DOI: 10.1109/TBIOM.2026.3694899
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