Morphing attack detection by secunet achieves excellent result in NIS
[Essen, Germany, 28 October 2022] 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. These notably 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 Recognition Vendor Test (FRVT)-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 FRVT-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. 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 and is confident that the next version submitted to NIST will achieve even better results.
secunet Security Networks AG
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secunet – Protecting Digital Infrastructures
secunet is Germany’s leading cybersecurity company. In an increasingly connected world, the company’s combination of products and consulting assures resilient digital infrastructures and the utmost protection for data, applications and digital identities. secunet specialises in areas with unique security requirements – such as cloud, IIoT, eGovernment and eHealth. With security solutions from secunet, companies can maintain the highest security standards in digitisation projects and advance their digital transformation.
Over 1000 experts strengthen the digital sovereignty of governments, businesses and society. secunet’s customers include federal ministries, more than 20 DAX-listed corporations as well as other national and international organisations. The company was established in 1997, is listed in the SDAX and generated revenues of around 337 million euros in 2021.
secunet is an IT security partner to the Federal Republic of Germany and a partner of the German Alliance for Cyber Security.
Further information can be found at https://www.secunet.com/en/.
secunet Security Networks AG
45138 Essen, Germany
Amtsgericht Essen HRB 13615
Axel Deininger (Vors.)
Dr. Kai Martius
Dr. Ralf Wintergerst
T FRVT-MORPH test