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Automatic speaker verification (ASV) systems use a playback detector tofilter out playback attacks and ensure verification reliability. Since currentplayback detection models are almost always trained using genuine andplayed-back speech, it may be possible to degrade their performance bytransforming the acoustic characteristics of the played-back speech close tothat of the genuine speech. One way to do this is to enhance speech "stolen"from the target speaker before playback. We tested the effectiveness of aplayback attack using this method by using the speech enhancement generativeadversarial network to transform acoustic characteristics. Experimental resultsshowed that use of this "enhanced stolen speech" method significantly increasesthe equal error rates for the baseline used in the ASVspoof 2017 challenge andfor a light convolutional neural network-based method. The results also showedthat its use degrades the performance of a Gaussian mixture model-universalbackground model-based ASV system. This type of attack is thus an urgentproblem needing to be solved.

Transforming acoustic characteristics to deceive playback spoofing countermeasures of speaker verification systems