Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach

Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach

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Currently, the use of biometric identification, automated or semiautomated, is a reality.For this reason, the number of attacks has increased in such systems.One of the most common biometric attacks is the presentation attack (PA) because it is relatively easy to perform.Automated border control (ABC) is a clear target for phishers.

Concerning biometric attacks, morphing is one of the most threatening attacks because authentication systems are usually unable to correctly detect them.In this attack, a fake face is generated with the morphing and blending of two different subjects (genuine and phisher), and the image result is stored in the copyright.These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks.This research work proposes The Pilgrimage to the Holy Face of Lucca in 1896: Documents and News from the Archive of the Congregation of the Holy Face a de-morphing architecture that is founded on a convolutional neural network (CNN) architecture.

This technique is based on the use of two images: the potentially morphed image stored in the copyright, and the snapshot of the person located in the ABC system.The goal of the de-morphing process is to unravel the chip image.If the chip image is a morphed one, the revealing process between the in vivo image and the morphed chip image will return a different facial identity to the person located in the ABC system, and the impostor will be uncovered in situ.If the chip image is a non-morphing image, the resulting image will be similar to a genuine passenger.

Therefore, the information obtained is considered at the border crossing.The equal error rate (EER) achieved is very low compared to the literature values published to date.The accomplished outcomes endorse a robust method that provides The lived experience and career of an opera singer who stutters high accuracy rates without taking into account the quality of images used.This key point is crucial to plausible deployment plans in areas such as ABC.