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Auflistung nach Autor:in "Marcel,Sébastien"

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  • Textdokument
    Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting
    (BIOSIG 2017, 2017) Scherhag,Ulrich; Nautsch,Andreas; Rathgeb,Christian; Gomez-Barrero,Marta; Veldhuis,Raymond N.J.; Spreeuwers,Luuk; Schils,Maikel; Maltoni,Davide; Grother,Patrick; Marcel,Sébastien; Breithaupt,Ralph; Ramachandra,Raghavendra; Busch,Christoph
    With the widespread deployment of biometric recognition systems, the interest in attacking these systems is increasing. One of the easiest ways to circumvent a biometric recognition system are so-called presentation attacks, in which artefacts are presented to the sensor to either impersonate another subject or avoid being recognised. In the recent past, the vulnerabilities of biometric systems to so-called morphing attacks have been unveiled. In such attacks, biometric samples of multiple subjects are merged in the signal or feature domain, in order to allow a successful verification of all contributing subjects against the morphed identity. Being a recent area of research, there is to date no standardised manner to evaluate the vulnerability of biometric systems to these attacks. Hence, it is not yet possible to establish a common benchmark between different morph detection algorithms. In this paper, we tackle this issue proposing new metrics for vulnerability reporting, which build upon our joint experience in researching this challenging attack scenario. In addition, recommendations on the assessment of morphing techniques and morphing detection metrics are given.
  • Textdokument
    On the Generalization of Fused Systems in Voice Presentation Attack Detection
    (BIOSIG 2017, 2017) Gonçalves,André R.; Korshunov,Pavel; Violato,Ricardo P.V.; Simões,Flávio O.; Marcel,Sébastien
    This paper describes presentation attack detection systems developed for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017). The submitted systems, using calibration and score fusion techniques, combine different sub-systems (up to 18), which are based on eight state of the art features and rely on Gaussian mixture models and feedforward neural network classifiers. The systems achieved the top five performances in the competition. We present the proposed systems and analyze the calibration and fusion strategies employed. To assess the systems’ generalization capacity, we evaluated it on an unrelated larger database recorded in Portuguese language, which is different from the English language used in the competition. These extended evaluation results show that the fusion-based system, although successful in the scope of the evaluation, lacks the ability to accurately discriminate genuine data from attacks in unknown conditions, which raises the question on how to assess the generalization ability of attack detection systems in practical application scenarios.
  • Textdokument
    What you can’t see can help you – extended-range imaging for 3D-mask presentation attack detection
    (BIOSIG 2017, 2017) Bhattacharjee,Sushil; Marcel,Sébastien
    High-quality custom-made 3D masks are increasing becoming a serious threat to face recognition systems. This threat is driven, in part, by the falling cost of manufacturing such masks. Research in face presentation-attack detection (PAD) in general, and also specifically for 3D-mask based attacks, has mostly concentrated on imagery in the visible-light range of wavelengths (RGB). We look beyond imagery in the visible-light spectrum to find potentially easier solutions for the challenge of face presentation-attack detection (PAD). In particular, we explore the use of nearinfrared (NIR) and thermal imagery to detect print-, replay-, and 3D-mask-attacks. This preliminary study shows that both NIR and thermal imagery can potentially simplify the task of face-PAD.