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Auflistung nach Schlagwort "Morphing"

1 - 4 von 4
  • 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.
  • Konferenzbeitrag
    Impact of Image Context for Single Deep Learning Face Morphing Attack Detection
    (BIOSIG 2023, 2023) Joana Pimenta, Iurii Medvedev
    The increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.
  • Konferenzbeitrag
    OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
    (BIOSIG 2022, 2022) Pedro C Neto, Tiago Gonçalves
    Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors.We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others.
  • Konferenzbeitrag
    Voice Morphing: Two Identities in One Voice
    (BIOSIG 2023, 2023) Sushanta K. Pani, Anurag Chowdhury
    In a biometric system, each biometric sample or template is typically associated with a single identity. However, recent research has demonstrated the possibility of generating "morph" biometric samples that can successfully match more than a single identity. Morph attacks are now recognized as a potential security threat to biometric systems. However, most morph attacks have been studied on biometric modalities operating in the image domain, such as face, fingerprint, and iris. In this preliminary work, we introduce Voice Identity Morphing (VIM) - a voice-based morph attack that can synthesize speech samples that impersonate the voice characteristics of a pair of individuals. Our experiments evaluate the vulnerabilities of two popular speaker recognition systems, ECAPA-TDNN and x-vector, to VIM, with a success rate (MMPMR) of over 80% at a false match rate of 1% on the Librispeech dataset.