  Spoofing countermeasure (CM) systems are critical in speaker verification;
they aim to discern spoofing attacks from bona fide speech trials. In practice,
however, acoustic condition variability in speech utterances may significantly
degrade the performance of CM systems. In this paper, we conduct a
cross-dataset study on several state-of-the-art CM systems and observe
significant performance degradation compared with their single-dataset
performance. Observing differences of average magnitude spectra of bona fide
utterances across the datasets, we hypothesize that channel mismatch among
these datasets is one important reason. We then verify it by demonstrating a
similar degradation of CM systems trained on original but evaluated on
channel-shifted data. Finally, we propose several channel robust strategies
(data augmentation, multi-task learning, adversarial learning) for CM systems,
and observe a significant performance improvement on cross-dataset experiments.
