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ANALYSING REPLAY SPOOFING COUNTERMEASURE PERFORMANCE UNDER VARIED CONDITIONS
Queen Mary Univ London, Sch EECS, London, England..
KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH. Queen Mary Univ London, Sch EECS, London, England..
Queen Mary Univ London, Sch EECS, London, England..
2018 (English)In: 2018 IEEE 28TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) / [ed] Pustelnik, N Ma, Z Tan, ZH Larsen, J, IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we aim to understand what makes replay spoofing detection difficult in the context of the ASVspoof 2017 corpus. We use FFT spectra, mel frequency cepstral coefficients (MFCC) and inverted MFCC (IMFCC) frontends and investigate different back-ends based on Convolutional Neural Networks (CNNs), Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs). On this database, we find that IMFCC frontend based systems show smaller equal error rate (EER) for high quality replay attacks but higher EER for low quality replay attacks in comparison to the baseline. However, we find that it is not straightforward to understand the influence of an acoustic environment (AE), a playback device (PD) and a recording device (RD) of a replay spoofing attack. One reason is the unavailability of metadata for genuine recordings. Second, it is difficult to account for the effects of the factors: AE, PD and RD, and their interactions. Finally, our frame-level analysis shows that the presence of cues (recording artefacts) in the first few frames of genuine signals (missing from replayed ones) influence class prediction.

Place, publisher, year, edition, pages
IEEE , 2018.
Series
IEEE International Workshop on Machine Learning for Signal Processing, ISSN 2161-0363
Keywords [en]
Automatic speaker verification, spoofing detection, replay attack, spoofing countermeasure
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-240055DOI: 10.1109/MLSP.2018.8516968ISI: 000450651000021Scopus ID: 2-s2.0-85057052903ISBN: 978-1-5386-5477-4 (print)OAI: oai:DiVA.org:kth-240055DiVA, id: diva2:1269192
Conference
IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), SEP 17-20, 2018, Aalborg, DENMARK
Note

QC 20181210

Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2019-04-29Bibliographically approved

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Sturm, Bob

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Citation style
  • apa
  • harvard1
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More styles
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  • de-DE
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
  • rtf