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Auflistung nach Schlagwort "Presentation Attack Detection"

1 - 5 von 5
  • Konferenzbeitrag
    Fake Face Detection Methods: Can They Be Generalized?
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Khodabakhsh, Ali; Ramachandra, Raghavendra; Raja, Kiran; Wasnik, Pankaj; Busch, Christoph
    With advancements in technology, it is now possible to create representations of human faces in a seamless manner for fake media, leveraging the large-scale availability of videos. These fake faces can be used to conduct personation attacks on the targeted subjects. Availability of open source software and a variety of commercial applications provides an opportunity to generate fake videos of a particular target subject in a number of ways. In this article, we evaluate the generalizability of the fake face detection methods through a series of studies to benchmark the detection accuracy. To this extent, we have collected a new database of more than 53;000 images, from 150 videos, originating from multiple sources of digitally generated fakes including Computer Graphics Image (CGI) generation and many tampering based approaches. In addition, we have also included images (with more than 3;200) from the predominantly used Swap-Face application that is commonly available on smart-phones. Extensive experiments are carried out using both texture-based handcrafted detection methods and deep learning based detection methods to find the suitability of detection methods. Through the set of evaluation, we attempt to answer if the current fake face detection methods can be generalizable.
  • Konferenzbeitrag
    Fingerprint Presentation Attack Detection using Laser Speckle Contrast Imaging
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Keilbach, Pascal; Kolberg, Jascha; Gomez-Barrero, Marta; Busch, Christoph; Langweg, Hanno
    With the increased deployment of biometric authentication systems, some security concerns have also arisen. In particular, presentation attacks directed to the capture device pose a severe threat. In order to prevent them, liveness features such as the blood flow can be utilised to develop presentation attack detection (PAD) mechanisms. In this context, laser speckle contrast imaging (LSCI) is a technology widely used in biomedical applications in order to visualise blood flow. We therefore propose a fingerprint PAD method based on textural information extracted from preprocessed LSCI images. Subsequently, a support vector machine is used for classification. In the experiments conducted on a database comprising 32 different artefacts, the results show that the proposed approach classifies correctly all bona fides. However, the LSCI technology experiences difficulties with thin and transparent overlay attacks.
  • Konferenzbeitrag
    Multi-algorithm Benchmark for Fingerprint Presentation Attack Detection with Laser Speckle Contrast Imaging
    (BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Kolberg, Jascha; Gomez-Barrero, Marta; Busch, Christoph
    The increased usage of biometric authentication systems has raised concerns regarding the security of components in a biometric system. As a consequence, preventing security issues related to presentation attacks targeting the biometric capture device are of utmost importance. To develop presentation attack detection (PAD) mechanisms, features confirming the liveness of the biometric characteristic such as the blood flow within the finger are needed. Utilising laser speckle contrast imaging (LSCI) to observe blood movement below the surface, we present an evaluation of different machine learning classifiers for fingerprint PAD. The experiments over a database comprising 35 different presentation attack instrument (PAI) species show that the detection performance varies depending on the utilised feature extraction method. A majority voting of selected classifiers and features achieves an APCER of 9% for a convenient BPCER of 0.05%.
  • 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
    When Facial Recognition Systems become Presentation Attack Detectors
    (BIOSIG 2022, 2022) Lazaro Janier Gonzalez-Soler, Kevin Abadi Barhaugen
    Recently, biometric systems (BSs) have experienced a broad development mainly due to the great success of deep learning approaches. Generally, most BS provide high security and efficiency. However, they are still vulnerable to attack presentations (APs). To overcome such security issues, these schemes include a Presentation Attack Detection (PAD) module which determines whether the input sample stems from an AP or a bona fide presentation (BP). Traditionally, most PAD subsystems assess the biometric sample prior to the recognition module. In this work, we evaluate to what extent the inverted combination, where the biometric recognition module filters samples prior to the assessment of a PAD mechanism, leads to an overall PAD performance improvement. The experimental evaluation conducted over two well-known databases including challenging attacks, reports a significant improvement in the detection performance when input samples were first filtered by the biometric recognition: only 1% of the APs are accepted while at most 5% BPs are rejected by the PAD subsystem.