3,296 research outputs found
Anti-spoofing Methods for Automatic SpeakerVerification System
Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.Comment: 12 pages, 0 figures, published in Springer Communications in Computer
and Information Science (CCIS) vol. 66
Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario
Face anti-spoofing is the key to preventing security breaches in biometric
recognition applications. Existing software-based and hardware-based face
liveness detection methods are effective in constrained environments or
designated datasets only. Deep learning method using RGB and infrared images
demands a large amount of training data for new attacks. In this paper, we
present a face anti-spoofing method in a real-world scenario by automatic
learning the physical characteristics in polarization images of a real face
compared to a deceptive attack. A computational framework is developed to
extract and classify the unique face features using convolutional neural
networks and SVM together. Our real-time polarized face anti-spoofing (PAAS)
detection method uses a on-chip integrated polarization imaging sensor with
optimized processing algorithms. Extensive experiments demonstrate the
advantages of the PAAS technique to counter diverse face spoofing attacks
(print, replay, mask) in uncontrolled indoor and outdoor conditions by learning
polarized face images of 33 people. A four-directional polarized face image
dataset is released to inspire future applications within biometric
anti-spoofing field.Comment: 14pages,8figure
Spoof detection using time-delay shallow neural network and feature switching
Detecting spoofed utterances is a fundamental problem in voice-based
biometrics. Spoofing can be performed either by logical accesses like speech
synthesis, voice conversion or by physical accesses such as replaying the
pre-recorded utterance. Inspired by the state-of-the-art \emph{x}-vector based
speaker verification approach, this paper proposes a time-delay shallow neural
network (TD-SNN) for spoof detection for both logical and physical access. The
novelty of the proposed TD-SNN system vis-a-vis conventional DNN systems is
that it can handle variable length utterances during testing. Performance of
the proposed TD-SNN systems and the baseline Gaussian mixture models (GMMs) is
analyzed on the ASV-spoof-2019 dataset. The performance of the systems is
measured in terms of the minimum normalized tandem detection cost function
(min-t-DCF). When studied with individual features, the TD-SNN system
consistently outperforms the GMM system for physical access. For logical
access, GMM surpasses TD-SNN systems for certain individual features. When
combined with the decision-level feature switching (DLFS) paradigm, the best
TD-SNN system outperforms the best baseline GMM system on evaluation data with
a relative improvement of 48.03\% and 49.47\% for both logical and physical
access, respectively
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