Reflection Analysis for Face Morphing Attack Detection

Seibold, Clemens; Hilsmann, Anna; Eisert, Peter

doi:10.23919/eusipco.2018.8553116

Cited by 41 publications

(23 citation statements)

References 30 publications

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“…Under the assumption that the images are intermediately stored during the morph creation process employing lossy compression algorithms, double compression artefacts can be analyzed [17], [43]. Furthermore, inconsistencies in the image, e.g., inconsistent illumination [44] or color values, might be evaluated.…”

Section: A Single Image Madmentioning

confidence: 99%

Deep Face Representations for Differential Morphing Attack Detection

Scherhag

Rathgeb

Merkle³

et al. 2020

IEEE Trans.Inform.Forensic Secur.

127

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The vulnerability of facial recognition systems to face morphing attacks is well known. Many different approaches for morphing attack detection (MAD) have been proposed in the scientific literature. However, the MAD algorithms proposed so far have mostly been trained and tested on datasets whose distributions of image characteristics are either very limited (e.g., only created with a single morphing tool) or rather unrealistic (e.g., no print-scan transformation). As a consequence, these methods easily overfit on certain image types and the results presented cannot be expected to apply to real-world scenarios. For example, the results of the latest NIST FRVT MORPH show that the majority of submitted MAD algorithms lacks robustness and performance when considering unseen and challenging datasets. In this work, subsets of the FERET and FRGCv2 face databases are used to create a realistic database for training and testing of MAD algorithms, containing a large number of ICAO-compliant bona fide facial images, corresponding unconstrained probe images, and morphed images created with four different face morphing tools. Furthermore, multiple post-processings are applied on the reference images, e.g., print-scan and JPEG2000 compression. On this database, previously proposed differential morphing algorithms are evaluated and compared. In addition, the application of deep face representations for differential MAD algorithms is investigated. It is shown that algorithms based on deep face representations can achieve very high detection performance (less than 3% D-EER) and robustness with respect to various post-processings. Finally, the limitations of the developed methods are analyzed.

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Section: A Single Image Madmentioning

confidence: 99%

Deep Face Representations for Differential Morphing Attack Detection

Scherhag

Rathgeb

Merkle³

et al. 2020

IEEE Trans.Inform.Forensic Secur.

127

View full text Add to dashboard Cite

show abstract

“…While superior accuracy has been reported for digital S-MAD with texture-based features, the main limitation of these techniques is in their generalisability across different image qualities, imaging sensors and print-scan processes [96]. b) Quality-based S-MAD: The quality-based techniques largely analyse image quality features by quantifying the image degradation to identify a given image as morphed or bona fide [45], [56], [57], [104], [111]. Several features, such as double-compression artifacts, photo response non-uniformity (PRNU), corner and edge distortions, reflection analysis and meta information in the images, are commonly used to detect distortion in a morphed image.…”

Section: Passport Application Formmentioning

confidence: 99%

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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“…Hence, depending on the image size and the employed compression algorithm the detection of JPEG double-compression artifacts might not be feasible. In [88], a morph detection method based on reflection analysis in face images is presented. The lightning direction is estimated based on reflections detected in the eyes of a potentially morphed image.…”

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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Reflection Analysis for Face Morphing Attack Detection

Cited by 41 publications

References 30 publications

Deep Face Representations for Differential Morphing Attack Detection

Deep Face Representations for Differential Morphing Attack Detection

Face Morphing Attack Generation and Detection: A Comprehensive Survey

Face Recognition Systems Under Morphing Attacks: A Survey

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