  Spoofing countermeasures aim to protect automatic speaker verification
systems from attempts to manipulate their reliability with the use of spoofed
speech signals. While results from the most recent ASVspoof 2019 evaluation
show great potential to detect most forms of attack, some continue to evade
detection. This paper reports the first application of RawNet2 to
anti-spoofing. RawNet2 ingests raw audio and has potential to learn cues that
are not detectable using more traditional countermeasure solutions. We describe
modifications made to the original RawNet2 architecture so that it can be
applied to anti-spoofing. For A17 attacks, our RawNet2 systems results are the
second-best reported, while the fusion of RawNet2 and baseline countermeasures
gives the second-best results reported for the full ASVspoof 2019 logical
access condition. Our results are reproducible with open source software.
