Mask Attack in Facial Recognition – Key Insights to Fortify Facial Identification Systems

Presentation attacks on facial recognition systems are increasing at an alarming rate. It is complicated to detect different forms of Presentation attacks without cutting-edge facial recognition software that can deter fraudulent attacks like the famous ‘Mask Attack’. Preventing Mask Attacks requires comprehensive knowledge about this malpractice and facial identity verification solutions like Facia must prioritize its prevention at the center of their Presentation attack detection capabilities.

What is a Mask Attack?

Mask Attack is a fraudulent practice in which a facial recognition system is deceived by wearing a facial mask that can be prosthetic, paper, or any other highly realistic mask that has the capability of outwitting the Facial Recognition technology. Facial recognition software works by detecting, verifying, and authenticating the facial features of an individual. For this purpose, they have unique technologies including a parametric approach to verify digital facial identities. 

Despite the stringent Face ID checks and robust anti-fraud features, Mask Attacks are also advancing through digitally crafted spoofing techniques, posing a challenge to swift and accurate facial verification.

Techniques of Mask Attack

A Mask Attack itself is a type of presentation attack. Several techniques are employed to carry out a mask attack to spoof an identity verification system with facial recognition. Here is a list of 6 major techniques used by fraudsters to spoof identities:

Liveness Detection – A Key to Mask Attack Detection

So far, we have understood the working and different dimensions of mask attacks. Despite its complicated web and its risky threat vectors, mask attacks can be detected and prevented. The core of detecting any type of identity spoof attack is liveness detection. This technique if incorporated accurately can effectively differentiate between an actual living person and a potential spoof (non-alive) face in front of the facial biometric scanning device. So far, almost every top-notch IDV solution follows liveness detection as a benchmark to scale their facial recognition systems.

Liveness Detection further has 2 types in the Identification of users through biometrics:

Active Liveness

Active Liveness check confirms that a real alive person is sitting in front of the camera for facial biometrics. It ensures that there is no picture replayed video or image attempting to bypass the facial identification checks.

Passive Liveness

In Passive Liveness, a recorded video or image from a phone or another display device is played before the biometric facial recognition system. Passive liveness will check and confirm if any video replay or picture mask replay attack is being carried out.

Liveness Detection and Biometric Matching Accuracy in Face Recognition

Usually, Liveness Detection is confused with another parameter of gauging a facial verification solution’s performance which is known as ‘Biometric Matching Accuracy’. 

Understanding Biometric Matching Accuracy

Biometric Matching Accuracy in facial recognition is one of the most important aspects of facial recognition. The main governing or standard-setting body for Identification solution providers is the National Institute of Standards and Technology (NIST).

Key Standards of NIST’s Facial Recognition Testing

• NIST tests different solution vendors who voluntarily provide their facial recognition algorithms to NIST for testing and analyzing their solution’s performance in different dimensions. 
• Under NIST’s benchmark-setting tests, it also analyzes the facial identity verification solution’s ability to detect morph and presentation attacks. 
• In September 2023, the National Institute of Standards and Technology (NIST) revealed its cutting-edge anti-spoofing algorithms after splitting its Face Recognition Vendor Test (FRVT) into 2 evaluations, biometrics, and facial analysis. In October 2023, Facial Identity Verification Solutions showed confidence in NIST evaluation on Presentation Attack Detection (PAD).
• NIST recently decided to continue its FRTE face mask benchmark on 1:1 algorithms.
• In 1:N identification through facial recognition, the False Positive Identification Rate (FPIR) is set to 0.003 for testing IDV solutions.
• NIST sets the benchmark for False Non-Match Rate (FNMR) at 0.000001

Facial Biometric Matching Accuracy while detecting mask attacks through facial recognition solutions is one of the best ways to prevent mask attacks for illicit gains. Banks, FIs, crypto exchanges, and other fintech firms require robust facial recognition for secure customer onboarding. 

Facia brings a unique web of swiftness and accuracy in detecting bypass attempts in facial IDV systems. Whether it’s mask attacks or morphing attempts, Facia is your weapon of choice in identity fraud prevention. It incorporates different cutting-edge technologies to identify the latest threat vectors. With being highly committed to complying with all NIST standards and other industry best practices, Facia envisions a secure digital onboarding for everyone.