Graduate School and Research Center in Digital Sciences

Spoofing attack detection using the non-linear fusion of sub-band classifiers

Tak, Hemlata; Patino, Jose; Nautsch, Andreas; Evans, Nicholas; Todisco, Massimiliano

Submitted to Interspeech 2020 conference, 20 May 2020

The threat of spoofing can pose a risk to the reliability of automatic speaker verification. Results from the biannual ASVspoof evaluations show that effective countermeasures demand front-ends designed specifically for the detection of spoofing artefacts. Given the diversity in spoofing attacks, ensemble methods are particularly effective. The work in this paper shows that a bank of very simple classifiers, each with a front-end tuned to the detection of different spoofing attacks and combined at the score level through non-linear fusion, can deliver superior performance than more sophisticated ensemble solutions that rely upon complex neural network architectures. Our comparatively simple approach outperforms all but 2 of the 48 systems submitted to the logical access condition of the most recent ASVspoof 2019 challenge.

Arxiv Bibtex

Title:Spoofing attack detection using the non-linear fusion of sub-band classifiers
Keywords:spoofing; sub-band countermeasures; presentation attack detection; ASVspoof; speaker verification
Type:Conference
Language:English
City:
Date:
Department:Digital Security
Eurecom ref:6270
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted to Interspeech 2020 conference, 20 May 2020 and is available at :
Bibtex: @inproceedings{EURECOM+6270, year = {2020}, title = {{S}poofing attack detection using the non-linear fusion of sub-band classifiers}, author = {{T}ak, {H}emlata and {P}atino, {J}ose and {N}autsch, {A}ndreas and {E}vans, {N}icholas and {T}odisco, {M}assimiliano}, booktitle = {{S}ubmitted to {I}nterspeech 2020 conference, 20 {M}ay 2020}, address = {}, month = {05}, url = {http://www.eurecom.fr/publication/6270} }
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