84 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
Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward
Malicious actors may seek to use different voice-spoofing attacks to fool ASV
systems and even use them for spreading misinformation. Various countermeasures
have been proposed to detect these spoofing attacks. Due to the extensive work
done on spoofing detection in automated speaker verification (ASV) systems in
the last 6-7 years, there is a need to classify the research and perform
qualitative and quantitative comparisons on state-of-the-art countermeasures.
Additionally, no existing survey paper has reviewed integrated solutions to
voice spoofing evaluation and speaker verification, adversarial/antiforensics
attacks on spoofing countermeasures, and ASV itself, or unified solutions to
detect multiple attacks using a single model. Further, no work has been done to
provide an apples-to-apples comparison of published countermeasures in order to
assess their generalizability by evaluating them across corpora. In this work,
we conduct a review of the literature on spoofing detection using hand-crafted
features, deep learning, end-to-end, and universal spoofing countermeasure
solutions to detect speech synthesis (SS), voice conversion (VC), and replay
attacks. Additionally, we also review integrated solutions to voice spoofing
evaluation and speaker verification, adversarial and anti-forensics attacks on
voice countermeasures, and ASV. The limitations and challenges of the existing
spoofing countermeasures are also presented. We report the performance of these
countermeasures on several datasets and evaluate them across corpora. For the
experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM,
CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of
the test bed can be found in our GitHub Repository
Bridging the Spoof Gap: A Unified Parallel Aggregation Network for Voice Presentation Attacks
Automatic Speaker Verification (ASV) systems are increasingly used in voice
bio-metrics for user authentication but are susceptible to logical and physical
spoofing attacks, posing security risks. Existing research mainly tackles
logical or physical attacks separately, leading to a gap in unified spoofing
detection. Moreover, when existing systems attempt to handle both types of
attacks, they often exhibit significant disparities in the Equal Error Rate
(EER). To bridge this gap, we present a Parallel Stacked Aggregation Network
that processes raw audio. Our approach employs a split-transform-aggregation
technique, dividing utterances into convolved representations, applying
transformations, and aggregating the results to identify logical (LA) and
physical (PA) spoofing attacks. Evaluation of the ASVspoof-2019 and VSDC
datasets shows the effectiveness of the proposed system. It outperforms
state-of-the-art solutions, displaying reduced EER disparities and superior
performance in detecting spoofing attacks. This highlights the proposed
method's generalizability and superiority. In a world increasingly reliant on
voice-based security, our unified spoofing detection system provides a robust
defense against a spectrum of voice spoofing attacks, safeguarding ASVs and
user data effectively
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
Deep learning has brought impressive progress in the study of both automatic
speaker verification (ASV) and spoofing countermeasures (CM). Although
solutions are mutually dependent, they have typically evolved as standalone
sub-systems whereby CM solutions are usually designed for a fixed ASV system.
The work reported in this paper aims to gauge the improvements in reliability
that can be gained from their closer integration. Results derived using the
popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a
state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation
protocol is extended with spoofed trials.%subjected to spoofing attacks.
However, even the straightforward integration of ASV and CM systems in the form
of score-sum and deep neural network-based fusion strategies reduce the EER to
1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification
(SASV) challenge has been formed to encourage greater attention to the
integration of ASV and CM systems as well as to provide a means to benchmark
different solutions.Comment: 8 pages, accepted by Odyssey 202
- …