A Face Morphing Detection Concept with a Frequency and a Spatial Domain Feature Space for Images on eMRTD

Neubert, Tom; Kraetzer, Christian; Dittmann, Jana

doi:10.1145/3335203.3335721

Cited by 21 publications

(12 citation statements)

References 25 publications

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“…Blind detectors usually consist of a feature extraction step followed by a classifier. The features can be handcrafted and describe effects such as the statistical properties of JPG coefficients after double compression [24] or the image impairment/change of spatial frequency distribution that arises from the warping and blending steps during the generation of a morphed face image [25,26]. Furthermore, the noise pattern characteristics of camera sensors [12] can be analyzed for detecting manipulated images.…”

Section: Related Workmentioning

confidence: 99%

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

2021

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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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Section: Related Workmentioning

confidence: 99%

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

2021

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“…Photo Response Non-Uniformity (PRNU) spectral analysis has also been proposed to analyze the alterations caused by morphing features [35]. In [26], the authors design a face morphing detector by combining spatial and frequency feature descriptors from an image. Fuzzy LBP in color channels of HSV and YCbCR color spaces are investigated in [33].…”

Section: Related Workmentioning

confidence: 99%

Differential Morph Face Detection using Discriminative Wavelet Sub-bands

Chaudhary¹,

Aghdaie²,

Soleymani³

et al. 2021

Preprint

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Face recognition systems are extremely vulnerable to morphing attacks, in which a morphed facial reference image can be successfully verified as two or more distinct identities. In this paper, we propose a morph attack detection algorithm that leverages an undecimated 2D Discrete Wavelet Transform (DWT) for identifying morphed face images. The core of our framework is that artifacts resulting from the morphing process that are not discernible in the image domain can be more easily identified in the spatial frequency domain. A discriminative wavelet sub-band can accentuate the disparity between a real and a morphed image. To this end, multi-level DWT is applied to all images, yielding 48 mid and high-frequency sub-bands each. The entropy distributions for each sub-band are calculated separately for both bona fide and morph images. For some of the sub-bands, there is a marked difference between the entropy of the sub-band in a bona fide image and the identical sub-band's entropy in a morphed image. Consequently, we employ Kullback-Liebler Divergence (KLD) to exploit these differences and isolate the sub-bands that are the most discriminative. We measure how discriminative a sub-band is by its KLD value and the 22 sub-bands with the highest KLD values are chosen for network training. Then, we train a deep Siamese neural network using these 22 selected sub-bands for differential morph attack detection. We examine the efficacy of discriminative wavelet sub-bands for morph attack detection and show that a deep neural network trained on these sub-bands can accurately identify morph imagery.

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“…Face forgery detection has gained momentum recently in the biometric community owing to its vast application, especially in commercial face recognition systems [27,19,53,37,18,10]. Photo-realistic forged images tamper with the functionality and integrity of security checkpoints, where, ideally, there must be a zero-tolerance policy to false acceptance [24,51,12,40]. Introduced in [12], facial morph images, as one of the categories of the forged face images, can bypass established automated face recognition systems, as well as border control officers, where both struggle to distinguish a bona fide image from a morphed one [25] due to delicacy in synthesizing morphed sam-Figure 1: Group Lasso regularization, as a representation learning, leads to selecting the most discriminative subbands for detecting a morphed image.…”

Section: Introductionmentioning

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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A Face Morphing Detection Concept with a Frequency and a Spatial Domain Feature Space for Images on eMRTD

Cited by 21 publications

References 25 publications

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

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

Differential Morph Face Detection using Discriminative Wavelet Sub-bands

Morph Detection Enhanced by Structured Group Sparsity

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