Face morph detection for unknown morphing algorithms and image sources: a multi‐scale block local binary pattern fusion approach

Scherhag, Ulrich; Kunze, J.; Rathgeb, Christian; Busch, Christoph

doi:10.1049/iet-bmt.2019.0206

Cited by 21 publications

(13 citation statements)

References 40 publications

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“…The use of texture descriptors has shown promising results e.g. for no-reference morphing attack detection as reported in [44]. Similarly, forensics-based detection methods, e.g.…”

Section: Manipulation Detection Scenariosmentioning

confidence: 90%

Digital Face Manipulation in Biometric Systems

Ibsen

Rathgeb

Fischer

et al. 2022

Advances in Computer Vision and Pattern Recognition

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Biometric technologies, in particular facerecognition, are employed in many personal, commercial, and governmental identity management systems around the world. The processing of digitally manipulated face images within a face recognition system may lead to false decisions and thus decrease the reliability of the decision system. This necessitates the development of manipulation detection modules which can be seamlessly integrated into the processing chain of face recognition systems. This chapter discusses the impact of face image manipulation on face recognition technologies. To this end, the basic processes and key components of biometric systems are briefly introduced with particular emphasis on facial recognition. Additionally, face manipulation detection scenarios and concepts of how to integrate detection methods to face recognition systems are discussed. In an experimental evaluation, it is shown that different types of face manipulation, i.e. retouching, face morphing, and swapping, can significantly affect the biometric performance of face recognition systems and hence impair their security. Eventually, this chapter provides an outlook on issues and challenges that face manipulation poses to face recognition technologies.

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“…The use of texture descriptors has shown promising results e.g. for no-reference morphing attack detection as reported in [44]. Similarly, forensics-based detection methods, e.g.…”

Section: Manipulation Detection Scenariosmentioning

confidence: 90%

Digital Face Manipulation in Biometric Systems

Ibsen

Rathgeb

Fischer

et al. 2022

Advances in Computer Vision and Pattern Recognition

Self Cite

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show abstract

“…The single image MAD approaches can be categorised into three classes: texture descriptors, e.g. in [20,24,26], forensic image analysis, e.g. in [23,32], and methods based on deep neural networks, e.g.…”

Section: Related Workmentioning

confidence: 99%

Face Morphing Attack Detection Methods

Scherhag

Rathgeb²,

Busch

2022

Handbook of Digital Face Manipulation and Detection

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Morphing attacks pose a serious threat to face recognition systems, especially in the border control scenario. In order to guarantee a secure operation of face recognition algorithms in the future, it is necessary to be able to reliably detect morphed facial images and thus be able to reject them during enrolment or verification. This chapter provides an overview of morphing attack detection algorithms and metrics to measure and compare their performance. Different concepts of morphing attack detection are introduced and state-of-the-art detection methods are evaluated in a comprehensive cross-database experiments considering various realistic image post-processings.

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“…Ferrara et al [13] introduced face demorphing to reverse the morphing process to detect altered images. Some research considers hand-crafted descriptors to detect morphed samples [11,29,34,28], where the fusion of different features has proven to be compelling for morph detection [34,32]. Deep morph detectors utilize the power of convolutional neural networks to detect morphed samples accurately [14,30,37,38,36,22].…”

Section: Morph Detectionmentioning

confidence: 99%

Morph Detection Enhanced by Structured Group Sparsity

Aghdaie¹,

Chaudhary²,

Soleymani³

et al. 2021

Preprint

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In this paper, we consider the challenge of face morphing attacks, which substantially undermine the integrity of face recognition systems such as those adopted for use in border protection agencies. Morph detection can be formulated as extracting fine-grained representations, where local discriminative features are harnessed for learning a hypothesis. To acquire discriminative features at different granularity as well as a decoupled spectral information, we leverage wavelet domain analysis to gain insight into the spatialfrequency content of a morphed face. As such, instead of using images in the RGB domain, we decompose every image into its wavelet sub-bands using 2D wavelet decomposition and a deep supervised feature selection scheme is employed to find the most discriminative wavelet sub-bands of input images. To this end, we train a Deep Neural Network (DNN) morph detector using the decomposed wavelet subbands of the morphed and bona fide images. In the training phase, our structured group sparsity-constrained DNN picks the most discriminative wavelet sub-bands out of all the sub-bands, with which we retrain our DNN, resulting in a precise detection of morphed images when inference is achieved on a probe image. The efficacy of our deep morph detector which is enhanced by structured group lasso is validated through experiments on three facial morph image databases, i.e., VISAPP17, LMA, and MorGAN.

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Face morph detection for unknown morphing algorithms and image sources: a multi‐scale block local binary pattern fusion approach

Cited by 21 publications

References 40 publications

Digital Face Manipulation in Biometric Systems

Digital Face Manipulation in Biometric Systems

Face Morphing Attack Detection Methods

Morph Detection Enhanced by Structured Group Sparsity

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