  Automatic speaker verification (ASV) systems use a playback detector to
filter out playback attacks and ensure verification reliability. Since current
playback detection models are almost always trained using genuine and
played-back speech, it may be possible to degrade their performance by
transforming the acoustic characteristics of the played-back speech close to
that 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 a
playback attack using this method by using the speech enhancement generative
adversarial network to transform acoustic characteristics. Experimental results
showed that use of this "enhanced stolen speech" method significantly increases
the equal error rates for the baseline used in the ASVspoof 2017 challenge and
for a light convolutional neural network-based method. The results also showed
that its use degrades the performance of a Gaussian mixture model-universal
background model-based ASV system. This type of attack is thus an urgent
problem needing to be solved.
