94 research outputs found
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
A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification
For practical automatic speaker verification (ASV) systems, replay attack
poses a true risk. By replaying a pre-recorded speech signal of the genuine
speaker, ASV systems tend to be easily fooled. An effective replay detection
method is therefore highly desirable. In this study, we investigate a major
difficulty in replay detection: the over-fitting problem caused by variability
factors in speech signal. An F-ratio probing tool is proposed and three
variability factors are investigated using this tool: speaker identity, speech
content and playback & recording device. The analysis shows that device is the
most influential factor that contributes the highest over-fitting risk. A
frequency warping approach is studied to alleviate the over-fitting problem, as
verified on the ASV-spoof 2017 database
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
Uncovering the Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects
Audio has become an increasingly crucial biometric modality due to its
ability to provide an intuitive way for humans to interact with machines. It is
currently being used for a range of applications, including person
authentication to banking to virtual assistants. Research has shown that these
systems are also susceptible to spoofing and attacks. Therefore, protecting
audio processing systems against fraudulent activities, such as identity theft,
financial fraud, and spreading misinformation, is of paramount importance. This
paper reviews the current state-of-the-art techniques for detecting audio
spoofing and discusses the current challenges along with open research
problems. The paper further highlights the importance of considering the
ethical and privacy implications of audio spoofing detection systems. Lastly,
the work aims to accentuate the need for building more robust and generalizable
methods, the integration of automatic speaker verification and countermeasure
systems, and better evaluation protocols.Comment: Accepted in IJCAI 202
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