Morphing attack detection by secunet

Morphing attack detection by secunet

secunet's algorithm achieves excellent result in NIST FATE-MORPH test
Biometrics and facial recognition have made border control more efficient and secure. However, there are types of fraud that still pose a challenge, both for border control officers and automated border control systems.

Update on FATE-MORPH test of NIST from November 16, 2022

For the second time secunet has submitted its improved algorithm for recognizing morphed facial images to the NIST FATE-MORPH test. The new secunet algorithm achieves even better and promising result. It achieves morph miss rates of 9% to 36% across all data sets with a false detection rate of 0.01 (1 in 100). At a false discovery rate of 0.02 (2 in 100), the morph miss rates drop to 4% to 22% across all evaluated datasets. This is an advance in morph detection and evidence that morph detection with a live probe image can be a practical approach.

These types of fraud include so-called morphing attacks, in which fraudsters use image editing software to merge their biometric passport photos into a single image that then looks like a composite of two people. When successfully applying this photo for a passport, both fraudsters are then recognizable as the person in the picture and can thus both use the same identity document. If the morphing is done well, neither the facial recognition software nor the officer at the border control counter will notice the difference between the person and the morphed image.

Software algorithms that recognize facial morphs during automated border control can increase border security substantially. Therefore, work on morphing attack detection (MAD) has increasingly been going on for several years by now.

secunet has recently submitted its algorithm for the recognition of morphed facial images to the independent and internationally recognized National Institute of Standards and Technology (NIST) Face Analysis Technology Evaluation (FATE)-MORPH test. secunet’s algorithm implements a differential morphing attack detection, which checks a potentially morphed facial image against a second, usually live captured and thus trusted image. The result from the NIST FATE-MORPH test shows that the recognition of morphed images has now reached a level of performance that allows operational use in the context of border control. Interested readers can view the full report here.

84% detection rate

Especially in the category of "High Quality Morphs", e.g., for manually generated morphs, good results were achieved: If set to a false positive rate of approximately 4% (meaning four out of every hundred facial images are misclassified as morphs and have to be manually verified), secunet’s algorithm spotted 84% of all morphed images. This presents a huge step in comparison to both human test subjects and previous algorithms.

The new algorithm is integrated throughout secunet’s entire border control portfolio and complements other security measures that are already available. Among others, this includes the automated border control system secunet easygate as well as the self-service system secunet easykiosk for pre-enrolment of passenger data. The new algorithm will also be included in the border control application secunet bocoa in combination with secunet easytower as facial image camera. All products and solutions from secunet provide sufficiently high-quality live images for the use as trusted comparison images for differential MAD.

As a next step, secunet has already identified areas of improvement to further optimize the performance of the algorithm.

Source: National Institute of Standards and Technology;
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