5 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
On the Resilience of Biometric Authentication Systems against Random Inputs
We assess the security of machine learning based biometric authentication
systems against an attacker who submits uniform random inputs, either as
feature vectors or raw inputs, in order to find an accepting sample of a target
user. The average false positive rate (FPR) of the system, i.e., the rate at
which an impostor is incorrectly accepted as the legitimate user, may be
interpreted as a measure of the success probability of such an attack. However,
we show that the success rate is often higher than the FPR. In particular, for
one reconstructed biometric system with an average FPR of 0.03, the success
rate was as high as 0.78. This has implications for the security of the system,
as an attacker with only the knowledge of the length of the feature space can
impersonate the user with less than 2 attempts on average. We provide detailed
analysis of why the attack is successful, and validate our results using four
different biometric modalities and four different machine learning classifiers.
Finally, we propose mitigation techniques that render such attacks ineffective,
with little to no effect on the accuracy of the system.Comment: Accepted by NDSS2020, 18 page
Face authentication using one-class support vector machines
This paper proposes a new method for personal identity verification
based the analysis of face images applying One Class Support
Vector Machines. This is a recently introduced kernel method to build a
unary classifier to be trained by using only positive examples, avoiding
the sensible choice of the impostor set typical of standard binary Support
Vector Machines. The features of this classifier and the application
to face-based identity verification are described and an implementation
presented. Several experiments have been performed on both standard
and proprietary databases. The tests performed, also in comparison with
a standard classifier built on Support Vector Machines, clearly show the
potential of the proposed approach