Face Recognition Systems Under Morphing Attacks: A Survey

Scherhag, Ulrich; Rathgeb, Christian; Merkle, Johannes; Breithaupt, Ralph; Busch, Christoph

doi:10.1109/access.2019.2899367

Cited by 187 publications

(159 citation statements)

References 106 publications

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“…All of them provide a high-quality performance with morphed images, but it should be noticed that researchers have to perform manual manipulation and generate a large enough number of images to achieve an adequate corpus for their research works. The well-known algorithm [22], [23] used in the literature is adapted to the studied problem and explained below. a) Given two pictures (see Figure 1(a)), 76 reference points are located in each one.…”

Section: A Morphing Processmentioning

confidence: 99%

“…In recent years, the wide use of ABC systems in airports has increased the attention and the study of the possible multiple menaces (e.g., presentation attack) as explained by the European Border and Coast Guard Agency (FRON-TEX) [17], [18]. These attacks incentivize the proliferation of algorithms about presentation attack detection (PAD) [19]- [21] and especially morphing attack detection (MAD) [13], [22] because it is a difficult paradigm to be detected.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A Morphing Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In many countries, face images used for the ePassport issuance are provided by the applicant. Based on this security gap in the process, the vulnerability of face recognition systems to so-called morphing attacks has been recently exposed [71], [72]. Similarly, facial retouching could be applied, which could significantly degrade the performance of a face recognition system, e.g., at automated border controls.…”

Section: Facial Retouchingmentioning

confidence: 99%

“…Benchmarks of state-of-the-art (commercial and open-source) face recognition systems are required to further investigate this hypothesis. However, recently researchers found that the improved generalizability of deep face recognition systems increases their vulnerability against attacks, e.g., spoofing attacks (also referred to as presentation attacks) [81] or face morphing attacks [71], [72]. Security aspects Facial beautification can have security implications on face recognition systems.…”

Section: Issues and Challengesmentioning

confidence: 99%

Impact and Detection of Facial Beautification in Face Recognition: An Overview

2019

Self Cite

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Facial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to face recognition are discussed, and related publications are revisited. Additionally, technical considerations and trade-offs of the surveyed methods are summarized along with open issues and challenges in the field. This survey is targeted to provide a comprehensive point of reference for biometric researchers and practitioners working in the field of face recognition, who aim at tackling challenges caused by facial beautification.

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“…Recently, the spectral analysis of Photo Response Non-Uniformity (PRNU) has been em-ployed [8] [22], to analyse modifications caused by the morphing procedure. For a quick overview of the existing state of the art based on morph attack detection are presented in [23].…”

Section: Related Workmentioning

confidence: 99%

Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

Venkatesh¹,

Ramachandra²,

Raja³

et al. 2020

Preprint

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Along with the deployment of the Face Recognition Systems (FRS), concerns were raised related to the vulnerability of those systems towards various attacks including morphed attacks. The morphed face attack involves two different face images in order to obtain via a morphing process a resulting attack image, which is sufficiently similar to both contributing data subjects. The obtained morphed image can successfully be verified against both subjects visually (by a human expert) and by a commercial FRS. The face morphing attack poses a severe security risk to the e-passport issuance process and to applications like border control, unless such attacks are detected and mitigated. In this work, we propose a new method to reliably detect a morphed face attack using a newly designed denoising framework. To this end, we design and introduce a new deep Multi-scale Context Aggregation Network (MS-CAN) to obtain denoised images, which is subsequently used to determine if an image is morphed or not. Extensive experiments are carried out on three different morphed face image datasets. The Morphing Attack Detection (MAD) performance of the proposed method is also benchmarked against 14 different state-of-the-art techniques using the ISO-IEC 30107-3 evaluation metrics. Based on the obtained quantitative results, the proposed method has indicated the best performance on all three datasets and also on cross-dataset experiments.

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Face Recognition Systems Under Morphing Attacks: A Survey

Cited by 187 publications

References 106 publications

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

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

Impact and Detection of Facial Beautification in Face Recognition: An Overview

Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

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