946 research outputs found
Biometric presentation attack detection: beyond the visible spectrum
The increased need for unattended authentication in
multiple scenarios has motivated a wide deployment of biometric
systems in the last few years. This has in turn led to the
disclosure of security concerns specifically related to biometric
systems. Among them, presentation attacks (PAs, i.e., attempts
to log into the system with a fake biometric characteristic or
presentation attack instrument) pose a severe threat to the
security of the system: any person could eventually fabricate
or order a gummy finger or face mask to impersonate someone
else. In this context, we present a novel fingerprint presentation
attack detection (PAD) scheme based on i) a new capture device
able to acquire images within the short wave infrared (SWIR)
spectrum, and i i) an in-depth analysis of several state-of-theart
techniques based on both handcrafted and deep learning
features. The approach is evaluated on a database comprising
over 4700 samples, stemming from 562 different subjects and
35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a
detection equal error rate (D-EER) as low as 1.35% even in a
realistic scenario where five different PAI species are considered
only for testing purposes (i.e., unknown attacks
Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey
The vulnerabilities of fingerprint authentication systems have raised
security concerns when adapting them to highly secure access-control
applications. Therefore, Fingerprint Presentation Attack Detection (FPAD)
methods are essential for ensuring reliable fingerprint authentication. Owing
to the lack of generation capacity of traditional handcrafted based approaches,
deep learning-based FPAD has become mainstream and has achieved remarkable
performance in the past decade. Existing reviews have focused more on
hand-cratfed rather than deep learning-based methods, which are outdated. To
stimulate future research, we will concentrate only on recent
deep-learning-based FPAD methods. In this paper, we first briefly introduce the
most common Presentation Attack Instruments (PAIs) and publicly available
fingerprint Presentation Attack (PA) datasets. We then describe the existing
deep-learning FPAD by categorizing them into contact, contactless, and
smartphone-based approaches. Finally, we conclude the paper by discussing the
open challenges at the current stage and emphasizing the potential future
perspective.Comment: 29 pages, submitted to ACM computing survey journa
Deep Composite Face Image Attacks: Generation, Vulnerability and Detection
Face manipulation attacks have drawn the attention of biometric researchers
because of their vulnerability to Face Recognition Systems (FRS). This paper
proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based
on the Generative Adversarial Networks (GANs). Given the face images from
contributory data subjects, the proposed CFIA method will independently
generate the segmented facial attributes, then blend them using transparent
masks to generate the CFIA samples. { The primary motivation for CFIA is to
utilize deep learning to generate facial attribute-based composite attacks,
which has been explored relatively less in the current literature.} We generate
different combinations of facial attributes resulting in unique CFIA
samples for each pair of contributory data subjects. Extensive experiments are
carried out on our newly generated CFIA dataset consisting of 1000 unique
identities with 2000 bona fide samples and 14000 CFIA samples, thus resulting
in an overall 16000 face image samples. We perform a sequence of experiments to
benchmark the vulnerability of CFIA to automatic FRS (based on both
deep-learning and commercial-off-the-shelf (COTS). We introduced a new metric
named Generalized Morphing Attack Potential (GMAP) to benchmark the
vulnerability effectively. Additional experiments are performed to compute the
perceptual quality of the generated CFIA samples. Finally, the CFIA detection
performance is presented using three different Face Morphing Attack Detection
(MAD) algorithms. The proposed CFIA method indicates good perceptual quality
based on the obtained results. Further, { FRS is vulnerable to CFIA} (much
higher than SOTA), making it difficult to detect by human observers and
automatic detection algorithms. Lastly, we performed experiments to detect the
CFIA samples using three different detection techniques automatically
Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking
Morphing attacks have posed a severe threat to Face Recognition System (FRS).
Despite the number of advancements reported in recent works, we note serious
open issues such as independent benchmarking, generalizability challenges and
considerations to age, gender, ethnicity that are inadequately addressed.
Morphing Attack Detection (MAD) algorithms often are prone to generalization
challenges as they are database dependent. The existing databases, mostly of
semi-public nature, lack in diversity in terms of ethnicity, various morphing
process and post-processing pipelines. Further, they do not reflect a realistic
operational scenario for Automated Border Control (ABC) and do not provide a
basis to test MAD on unseen data, in order to benchmark the robustness of
algorithms. In this work, we present a new sequestered dataset for facilitating
the advancements of MAD where the algorithms can be tested on unseen data in an
effort to better generalize. The newly constructed dataset consists of facial
images from 150 subjects from various ethnicities, age-groups and both genders.
In order to challenge the existing MAD algorithms, the morphed images are with
careful subject pre-selection created from the contributing images, and further
post-processed to remove morphing artifacts. The images are also printed and
scanned to remove all digital cues and to simulate a realistic challenge for
MAD algorithms. Further, we present a new online evaluation platform to test
algorithms on sequestered data. With the platform we can benchmark the morph
detection performance and study the generalization ability. This work also
presents a detailed analysis on various subsets of sequestered data and
outlines open challenges for future directions in MAD research.Comment: This paper is a pre-print. The article is accepted for publication in
IEEE Transactions on Information Forensics and Security (TIFS
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