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

Venkatesh, Sushma; Zhang, Haoyu; Ramachandra, Raghavendra; Raja, Kiran B.; Damer, Naser; Busch, Christoph

doi:10.1109/iwbf49977.2020.9107970

Cited by 83 publications

(110 citation statements)

References 17 publications

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“…Recent improvements in deep learning-based techniques have given rise to morph generation approaches based on generative adversarial networks (GANs) [41] [125]. In general, GAN-based methods synthesise morphed images that are generated by sampling two facial images in the latent space of the deep learning network.…”

Section: B Deep Learning-based Morph Generationmentioning

confidence: 99%

“…The generator is trained to generate images with dimensions of 64 × 64 pixels. Another recent approach based on StyleGAN architecture [24], [125] has improved the morph generation process both by increasing the spatial size to 1024×1024 and by increasing face quality. The pre-trained StyleGAN achieves this by embedding the images in the intermediate latent space.…”

Section: B Deep Learning-based Morph Generationmentioning

confidence: 99%

“…This database is private and has only digital morphed face data. Another database by Venkatesh et al [125] employs deep learning-based morph generation. The authors employed the StyleGAN network to generate synthetic morph images by mapping the input images into the latent space.…”

Section: Databases For Morphing Attack Detectionmentioning

confidence: 99%

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Face Morphing Attack Generation and Detection: A Comprehensive Survey

Venkatesh

Ramachandra

Raja

et al. 2021

IEEE Trans. Technol. Soc.

Self Cite

100

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Face recognition has been successfully deployed in real-time applications, including secure applications such as border control. The vulnerability of face recognition systems (FRSs) to various kinds of attacks (both direct and indirect attacks) and face morphing attacks has received great interest from the biometric community. The goal of a morphing attack is to subvert an FRS at an automatic border control (ABC) gate by presenting an electronic machine-readable travel document (eMRTD) or e-passport that is obtained based on a morphed face image. Since the application process for an e-passport in the majority of countries requires a passport photo to be presented by the applicant, a malicious actor and an accomplice can generate a morphed face image to obtain the e-passport. An e-passport with a morphed face image can be used by both the malicious actor and the accomplice to cross a border, as the morphed face image can be verified against both of them. This can result in a significant threat, as a malicious actor can cross the border without revealing the trace of his/her criminal background, while the details of the accomplice are recorded in the log of the access control system. This survey aims to present a systematic overview of the progress made in the area of face morphing in terms of both morph generation and morph detection. In this paper, we describe and illustrate various aspects of face morphing attacks, including different techniques for generating morphed face images and state-of-the-art morph attack detection (MAD) algorithms based on a stringent taxonomy as well as the availability of public databases, which allow us to benchmark new MAD algorithms in a reproducible manner. The outcomes of competitions and benchmarking, vulnerability assessments and performance evaluation metrics are also provided in a comprehensive manner. Furthermore, we discuss the open challenges and potential future areas that need to be addressed in the evolving field of biometrics.

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Section: B Deep Learning-based Morph Generationmentioning

confidence: 99%

Section: B Deep Learning-based Morph Generationmentioning

confidence: 99%

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Face Morphing Attack Generation and Detection: A Comprehensive Survey

Venkatesh

Ramachandra

Raja

et al. 2021

IEEE Trans. Technol. Soc.

Self Cite

100

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“…Moreover, we plan to extend this framework such that it can be used without having a reference image. Without having to rely on a reference image, we would also be able to use the framework for morphed face images generated by Generative Adversarial Networks [29].…”

Section: Summary and Discussionmentioning

confidence: 99%

Reflection Analysis for Face Morphing Attack Detection

Seibold

Hilsmann

Eisert

2018

2018 26th European Signal Processing Conference (EUSIPCO)

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A facial morph is a synthetically created image of a face that looks similar to two different individuals and can even trick biometric facial recognition systems into recognizing both individuals. This attack is known as face morphing attack. The process of creating such a facial morph is well documented and a lot of tutorials and software to create them are freely available. Therefore, it is mandatory to be able to detect this kind of fraud to ensure the integrity of the face as reliable biometric feature. In this work, we study the effects of face morphing on the physically correctness of the illumination. We estimate the direction to the light sources based on specular highlights in the eyes and use them to generate a synthetic map for highlights on the skin. This map is compared with the highlights in the image that is suspected to be a fraud. Morphing faces with different geometries, a bad alignment of the source images or using images with different illuminations, can lead to inconsistencies in reflections that indicate the existence of a morphing attack.

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“…The generator was trained to generate images with the dimension 64 × 64 pixels which is a key limiting factor of the attack, as most commercial FRS will reject images that do not meet the ICAO standard that requires a minimum Inter-Eye Distance (IED) of 90 pixels. The empirical evaluation of generated morph images using MorGAN in a vulnerability analysis against two commercial FRS indicated that those MorGAN morphs fail to meet both quality standards and the verification threshold of the FRS [1]. Motivated to address the deficiency of the MorGAN architecture, in our recent work [1] 1 we proposed an approach based on the StyleGAN architecture [29] to increase the spatial dimension to 1024 × 1024 and thus to improve the face image quality.…”

Section: Gan Based Face Morph Generationmentioning

confidence: 99%

MIPGAN—Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

Zhang

Venkatesh

Ramachandra

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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Face morphing attacks target to circumvent Face Recognition Systems (FRS) by employing face images derived from multiple data subjects (e.g., accomplices and malicious actors). Morphed images can be verified against contributing data subjects with a reasonable success rate, given they have a high degree of facial resemblance. The success of morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating strong attacks extending our earlier framework for generating face morphs. We present a new approach using an Identity Prior Driven Generative Adversarial Network, which we refer to as MIPGAN (Morphing through Identity Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution. We demonstrate the proposed approach's applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based Face Recognition System (FRS) and demonstrate the success rate of attacks. Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the proposed approach of morph generation poses a high threat to FRS.

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

Cited by 83 publications

References 17 publications

Face Morphing Attack Generation and Detection: A Comprehensive Survey

Face Morphing Attack Generation and Detection: A Comprehensive Survey

Reflection Analysis for Face Morphing Attack Detection

MIPGAN—Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

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