Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps

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. INDEX TERMS ABC, biometric systems, de-morphing, neural networks, MAD.

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“…4 (d)). The result of merging all triangles is the average image ( Figure 5(a)), and this process is called the warping process [43], [77], [78].…”

Section: A Morphing Processmentioning

confidence: 99%

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

et al. 2020

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“…With respect to the above attack scenario, it is stressed that a detection of morphed face images becomes even more challenging if images are printed and scanned. Hildebrandt et al [8] suggest employing generic image forgery detection techniques, in particular, multicompression anomaly detection, to reliably detect morphed facial images. Kraetzer et al [12] evaluate the feasibility of detecting facial morphs with key-point descriptors and edge operators.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, attacks on the face, fingerprint and iris recognition systems based on morphed biometric images and templates have been presented [5][6][7][8][9]. Morphed biometric information is an artificially generated sample or template, which blends the biometric information of two different data subjects into one.…”

Section: Introductionmentioning

confidence: 99%

Predicting the vulnerability of biometric systems to attacks based on morphed biometric information

et al. 2018

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Morphing techniques can be used to create artificial biometric samples or templates, which resemble the biometric information of two or more individuals in signal and feature domain. If morphed biometric samples or templates are infiltrated to a biometric recognition system, the subjects contributing to the morphed sample can be both successfully verified against a single enrolled template. Hence, the unique link between individuals and their biometric reference data is not warranted. This leads to serious security gaps in biometric applications, in particular, the issuance and verification process of electronic travel documents. Recently, different biometric systems have been attacked using morphed biometric samples. However, so far a systematic approach to predict the vulnerability of the system to such attacks has not been proposed. In this work, the authors present a framework to evaluate the vulnerability of biometric systems to attacks using morphed biometric information. Based on a biometric system's mated/non-mated score distribution and its decision threshold, a theoretical vulnerability assessment is proposed. In an experimental evaluation, the vulnerability of a face and an iris recognition system is quantified based on the presented framework. Obtained results are verified against real attacks based on morphed face images and morphed iris-based templates.

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“…Scherhag et al [11] analyzed multiple general purpose image feature extractors in this differential scenario. Further, Hildebrandt et al [12] suggest to employ generic image forgery detection techniques, in particular multi-compression anomaly detection, to reliably detect morphed facial images. Kraetzer et al [13] evaluate the feasibility of detecting facial morphs with keypoint descriptors and edge operators.…”

Section: Introductionmentioning

confidence: 99%

Detecting Morphed Face Images Using Facial Landmarks

Scherhag

Budhrani

Gomez‐Barrero

et al. 2018

Lecture Notes in Computer Science

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Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps

Cited by 46 publications

References 11 publications

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

Predicting the vulnerability of biometric systems to attacks based on morphed biometric information

Detecting Morphed Face Images Using Facial Landmarks

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