Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting

Scherhag, Ulrich; Nautsch, Andreas; Rathgeb, Christian; Gomez‐Barrero, Marta; Veldhuis, Raymond; Spreeuwers, Luuk; Schils, Maikel; Maltoni, Davide; Grother, Patrick J.; Marcel, Sébastien; Breithaupt, Ralph; Ramachandra, Raghavendra; Busch, Christoph

doi:10.23919/biosig.2017.8053499

Cited by 118 publications

(95 citation statements)

References 7 publications

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“…Additionally, to guarantee high quality of the newly generated dataset constraints of high quality illumination and no pose is imposed before creating the morphs. The guidelines laid out in earlier works [12] [16] are followed to obtain a database of high relevance for morphing attack detection.…”

Section: A Database Generationmentioning

confidence: 99%

Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection

Venkatesh

Zhang

Ramachandra

et al. 2020

2020 8th International Workshop on Biometrics and Forensics (IWBF)

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The primary objective of face morphing is to combine face images of different data subjects (e.g. a malicious actor and an accomplice) to generate a face image that can be equally verified for both contributing data subjects. In this paper, we propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN)-StyleGAN. In contrast to earlier works, we generate realistic morphs of both high-quality and high resolution of 1024×1024 pixels. With the newly created morphing dataset of 2500 morphed face images, we pose a critical question in this work. (i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs? Seeking an answer, we benchmark the vulnerability of a Commercial-Off-The-Shelf FRS (COTS) and a deep learning-based FRS (ArcFace). This work also benchmarks the detection approaches for both GAN generated morphs against the landmark based morphs using established Morphing Attack Detection (MAD) schemes.

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Section: A Database Generationmentioning

confidence: 99%

Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection

Venkatesh

Zhang

Ramachandra

et al. 2020

2020 8th International Workshop on Biometrics and Forensics (IWBF)

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“…One approach is to develop increasingly sophisticated computer methods for morph detection (e.g. Makrushin, Neubert, & Dittmann, 2017; Neubert, 2017; Raghavendra, Raja, Venkatesh, & Busch, 2017a, 2017b; Scherhag, Nautsch, et al, 2017; Scherhag, Raghavendra, et al, 2017; Seibold, Samek, Hilsmann, & Eisert, 2017, 2018). For example, inconsistencies between the reflections visible in the eyes and skin could signal a morphed image (Seibold, Hilsmann, & Eisert, 2018).…”

Section: Introductionmentioning

confidence: 99%

Face morphing attacks: Investigating detection with humans and computers

et al. 2019

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Background In recent years, fraudsters have begun to use readily accessible digital manipulation techniques in order to carry out face morphing attacks. By submitting a morph image (a 50/50 average of two people’s faces) for inclusion in an official document such as a passport, it might be possible that both people sufficiently resemble the morph that they are each able to use the resulting genuine ID document. Limited research with low-quality morphs has shown that human detection rates were poor but that training methods can improve performance. Here, we investigate human and computer performance with high-quality morphs, comparable with those expected to be used by criminals. Results Over four experiments, we found that people were highly error-prone when detecting morphs and that training did not produce improvements. In a live matching task, morphs were accepted at levels suggesting they represent a significant concern for security agencies and detection was again error-prone. Finally, we found that a simple computer model outperformed our human participants. Conclusions Taken together, these results reinforce the idea that advanced computational techniques could prove more reliable than training people when fighting these types of morphing attacks. Our findings have important implications for security authorities worldwide. Electronic supplementary material The online version of this article (10.1186/s41235-019-0181-4) contains supplementary material, which is available to authorized users.

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“…In addition to the aforementioned metrics and in order to evaluate the quality of the generated database, the Mated Morph Presentation Match Rate as well as the Relative Morph Match Rate (RMMR) [18] were calculated, which indicates the vulnerability of the FRS with respect to the attack. Employing the Cognitec FaceVACS-SDK [19] a ProdAvg-MMPMR of 96.7% ProdAvg-RMMR of 97.7% was computed for testing and training set.…”

Section: Experiments and Resultsmentioning

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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Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting

Cited by 118 publications

References 7 publications

Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection

Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? - Vulnerability and Detection

Face morphing attacks: Investigating detection with humans and computers

Detecting Morphed Face Images Using Facial Landmarks

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