Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach
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...
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doaj-58ebba2e0ceb43db9e51b5c9925fb90d2021-03-30T01:34:04ZengIEEEIEEE Access2169-35362020-01-018923019231310.1109/ACCESS.2020.29941129091520Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing ApproachDavid Ortega-Delcampo0Cristina Conde1Daniel Palacios-Alonso2https://orcid.org/0000-0001-6063-4898Enrique Cabello3Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Campus de Móstoles, Madrid, SpainEscuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Campus de Móstoles, Madrid, SpainEscuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Campus de Móstoles, Madrid, SpainEscuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Campus de Móstoles, Madrid, SpainCurrently, 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 passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes 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 passport, 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 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.https://ieeexplore.ieee.org/document/9091520/ABCbiometric systemsde-morphingneural networksMAD |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Ortega-Delcampo Cristina Conde Daniel Palacios-Alonso Enrique Cabello |
spellingShingle |
David Ortega-Delcampo Cristina Conde Daniel Palacios-Alonso Enrique Cabello Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach IEEE Access ABC biometric systems de-morphing neural networks MAD |
author_facet |
David Ortega-Delcampo Cristina Conde Daniel Palacios-Alonso Enrique Cabello |
author_sort |
David Ortega-Delcampo |
title |
Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach |
title_short |
Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach |
title_full |
Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach |
title_fullStr |
Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach |
title_full_unstemmed |
Border Control Morphing Attack Detection With a Convolutional Neural Network De-Morphing Approach |
title_sort |
border control morphing attack detection with a convolutional neural network de-morphing approach |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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 passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes 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 passport, 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 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. |
topic |
ABC biometric systems de-morphing neural networks MAD |
url |
https://ieeexplore.ieee.org/document/9091520/ |
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