Automatic Detection of Image Morphing by Topology-based Analysis

Jassim, Sabah; Asaad, Aras

doi:10.23919/eusipco.2018.8553317

Cited by 17 publications

(15 citation statements)

References 16 publications

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“…The features extracted by texture descriptors can be further processed. A more complex method for morphing detection is proposed in [75] and [76], where a Vietoris-Rips complex is built of the responses of uniform LBP extractors on the image. In [100], a high detection performance was shown for a linear SVM trained on high-dimensional LBP features [105] extracted from the FEI database [87].…”

Section: ) No-reference Morphing Attack Detectionmentioning

confidence: 99%

Face Recognition Systems Under Morphing Attacks: A Survey

et al. 2019

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Recently, researchers found that the intended generalizability of (deep) face recognition systems increases their vulnerability against attacks. In particular, the attacks based on morphed face images pose a severe security risk to face recognition systems. In the last few years, the topic of (face) image morphing and automated morphing attack detection has sparked the interest of several research laboratories working in the field of biometrics and many different approaches have been published. In this paper, a conceptual categorization and metrics for an evaluation of such methods are presented, followed by a comprehensive survey of relevant publications. In addition, technical considerations and tradeoffs of the surveyed methods are discussed along with open issues and challenges in the field. INDEX TERMS Biometrics, face morphing attack, face recognition, image morphing, morphing attack detection.

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Section: ) No-reference Morphing Attack Detectionmentioning

confidence: 99%

Face Recognition Systems Under Morphing Attacks: A Survey

et al. 2019

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“…On the one hand, there are research works that rely on micro-textures which use some features such as the local binary pattern (LBP - [40]) in [25], [31], [39], [41]- [43], or weighted local magnitude pattern (WLMP) which is proposed and explained in [44]. On the other hand, there are research works based on analysis of descriptors which use the scale invariant feature transform (SIFT - [45]) in [46], binarized statistical image features (BSIF - [47]) in [27], and speeded-up robust features (SURF - [48]) in [34].…”

Section: Previous Workmentioning

confidence: 99%

“…4 (d)). The result of merging all triangles is the average image ( Figure 5(a)), and this process is called the warping process [43], [77], [78].…”

Section: A Morphing Processmentioning

confidence: 99%

Border Control Morphing Attack Detection with a Convolutional Neural Network De-morphing Approach

et al. 2020

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Currently, the use of biometric identification, automated or semiautomated, is a reality. For this reason, the number of attacks has increased in such systems. One of the most common biometric attacks is the presentation attack (PA) because it is relatively easy to perform. Automated border control (ABC) is a clear target for phishers. Concerning biometric attacks, morphing is one of the most threatening attacks because authentication systems are usually unable to correctly detect them. In this attack, a fake face is generated with the morphing and blending of two different subjects (genuine and phisher), and the image result is stored in the passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes a de-morphing architecture that is founded on a convolutional neural network (CNN) architecture. This technique is based on the use of two images: the potentially morphed image stored in the passport, and the snapshot of the person located in the ABC system. The goal of the de-morphing process is to unravel the chip image. If the chip image is a morphed one, the revealing process between the in vivo image and the morphed chip image will return a different facial identity to the person located in the ABC system, and the impostor will be uncovered in situ. If the chip image is a non-morphing image, the resulting image will be similar to a genuine passenger. Therefore, the information obtained is considered at the border crossing. The equal error rate (EER) achieved is very low compared to the literature values published to date. The accomplished outcomes endorse a robust method that provides high accuracy rates without taking into account the quality of images used. This key point is crucial to plausible deployment plans in areas such as ABC. INDEX TERMS ABC, biometric systems, de-morphing, neural networks, MAD.

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“…An extension of these algorithms to several colour channels [13, 14] or higher dimensions [22] can lead to further improvements. Other texture descriptors, such as unified LBPs (ULBPs) [17, 18] or weighted local magnitude patterns (WLMPs) [16] have also been tested.…”

Section: Related Workmentioning

confidence: 99%