  It becomes urgent to design effective anti-spoofing algorithms for vulnerable
automatic speaker verification systems due to the advancement of high-quality
playback devices. Current studies mainly treat anti-spoofing as a binary
classification problem between bonafide and spoofed utterances, while lack of
indistinguishable samples makes it difficult to train a robust spoofing
detector. In this paper, we argue that for anti-spoofing, it needs more
attention for indistinguishable samples over easily-classified ones in the
modeling process, to make correct discrimination a top priority. Therefore, to
mitigate the data discrepancy between training and inference, we propose D3M,
to leverage a balanced focal loss function as the training objective to
dynamically scale the loss based on the traits of the sample itself. Besides,
in the experiments, we select three kinds of features that contain both
magnitude-based and phase-based information to form complementary and
informative features. Experimental results on the ASVspoof2019 dataset
demonstrate the superiority of the proposed methods by comparison between our
systems and top-performing ones. Systems trained with the balanced focal loss
perform significantly better than conventional cross-entropy loss. With
complementary features, our fusion system with only three kinds of features
outperforms other systems containing five or more complex single models by
22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124
and 0.55% respectively. Furthermore, we present and discuss the evaluation
results on real replay data apart from the simulated ASVspoof2019 data,
indicating that research for anti-spoofing still has a long way to go. Source
code, analysis data, and other details are publicly available at
https://github.com/asvspoof/D3M.
