4 research outputs found
Attacking a smartphone biometric fingerprint system:a novice’s approach
Biometric systems on mobile devices are an
increasingly ubiquitous method for identity verification. The
majority of contemporary devices have an embedded fingerprint
sensor which may be used for a variety of transactions including
unlock a device or sanction a payment. In this study we explore
how easy it is to successfully attack a fingerprint system using a
fake finger manufactured from commonly available materials.
Importantly our attackers were novices to producing the fingers
and were also constrained by time. Our study shows the relative
ease that modern devices can be attacked and the material
combinations that lead to these attacks
Attacking a smartphone biometric fingerprint system: a novice's approach
Biometric systems on mobile devices are an increasingly ubiquitous method for identity verification. The majority of contemporary devices have an embedded fingerprint sensor which may be used for a variety of transactions including unlock a device or sanction a payment. In this study we explore how easy it is to successfully attack a fingerprint system using a fake finger manufactured from commonly available materials. Importantly our attackers were novices to producing the fingers and were also constrained by time. Our study shows the relative ease that modern devices can be attacked and the material combinations that lead to these attacks
SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment
The paper presents a summary of the 2020 Sclera
Segmentation Benchmarking Competition (SSBC), the 7th in
the series of group benchmarking efforts centred around the
problem of sclera segmentation. Different from previous
editions, the goal of SSBC 2020 was to evaluate the
performance of sclera-segmentation models on images
captured with mobile devices. The competition was used as a
platform to assess the sensitivity of existing models to i)
differences in mobile devices used for image capture and ii)
changes in the ambient acquisition conditions. 26 research
groups registered for SSBC 2020, out of which 13 took part
in the final round and submitted a total of 16 segmentation
models for scoring. These included a wide variety of
deep-learning solutions as well as one approach based on
standard image processing techniques. Experiments were
conducted with three recent datasets. Most of the
segmentation models achieved relatively consistent
performance across images captured with different mobile
devices (with slight differences across devices), but
struggled most with low-quality images captured in
challenging ambient conditions, i.e., in an indoor
environment and with poor lighting