Dempster-Shafer Theory for Fusing Face Morphing Detectors

Makrushin, Andrey; Kraetzer, Christian; Dittmann, Jana; Seibold, Clemens; Hilsmann, Anna; Eisert, Peter

doi:10.23919/eusipco.2019.8902533

Cited by 12 publications

(13 citation statements)

References 19 publications

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“…In this work, we focus on single image based morphing attack detection (S-MAD) as it perfectly suits our dataset. MAD has been widely addressed in the literature by developing the techniques based on both deep learning [57], [58], [59] [60] [61] and non-deep learning [62] [19] [63] [64] approaches. Readers can refer to [65] for an exclusive survey on face MAD.…”

Section: Morphing Attack Detection Potentialmentioning

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.

114

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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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Section: Morphing Attack Detection Potentialmentioning

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.

114

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“…Full Name: [92]. Following the initial work, several approaches were proposed, as indicated in [30], [71], [106], [107] have also been widely explored in the reported works. The use of micro-texture-based methods has shown reasonable performance on both digital and print-scan types of S-MAD.…”

Section: Passport Application Formmentioning

confidence: 99%

“…The first work in this direction was based on using pre-trained networks such as AlexNet and VGG18, in which the features are fused and classified to detect a morphing attack [93]. Following this, several deep CNN pre-trained networks, such as AlexNet, VGG19, VGG-Face16, GoogleNet, ResNet18, ResNet150, ResNet50, VGG-Face2 and OpenFace [52], [43], [122], [112], [107], [108], [113], [71], [112], have been explored. Although deep CNNs have shown better performance than hand-crafted texture descriptor-based MAD methods on both digital and print-scan data, the generalisation capability of these approaches is limited across different print and scan datasets [110].…”

Section: Passport Application Formmentioning

confidence: 99%

“…e) Hybrid S-MAD: Hybrid approaches are based on multiple feature extractors or classifiers that are combined to detect face morphing attacks. Several approaches have been proposed that combine features, morphing detection scores or decision scores [121], [43], [71], [107], [94], [95]. As these approaches combine more than one feature extractor and classifier, the MAD performance is generally superior to that of single-mode MAD techniques.…”

Section: Passport Application Formmentioning

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.

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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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“…Such learned features are very powerful, but it is difficult to analyze what they describe or represent. Other blind detection methods are based on compositions of different detectors and fuse their predictions to obtain a more robust and accurate face morphing attack detector [28,29].…”

Section: Related Workmentioning

confidence: 99%

Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

2021

Self Cite

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Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates. Deep Neural Network (DNN)-based detectors have shown remarkable results, but without further investigations their decision-making process is not transparent. In contrast to approaches based on hand-crafted features, DNNs have to be analyzed in complex experiments to know which characteristics or structures are generally used to distinguish between morphed and genuine face images or considered for an individual morphed face image. In this paper, we present Feature Focus, a new transparent face morphing detector based on a modified VGG-A architecture and an additional feature shaping loss function, as well as Focused Layer-wise Relevance Propagation (FLRP), an extension of LRP. FLRP in combination with the Feature Focus detector forms a reliable and accurate explainability component. We study the advantages of the new detector compared to other DNN-based approaches and evaluate LRP and FLRP regarding their suitability for highlighting traces of image manipulation from face morphing. To this end, we use partial morphs which contain morphing artifacts in predefined areas only and analyze how much of the overall relevance each method assigns to these areas.

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Dempster-Shafer Theory for Fusing Face Morphing Detectors

Cited by 12 publications

References 19 publications

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

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

Face Morphing Attack Generation and Detection: A Comprehensive Survey

Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

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