ISCA Archive Odyssey 2022
ISCA Archive Odyssey 2022

Robust Cross-SubBand Countermeasure Against Replay Attacks

Jingze Lu, Yuxiang Zhang, Wenchao Wang, Pengyuan Zhang

In the ASVspoof2021 physical access (PA) task, due to the mismatch between the simulated training data and the evaluation data from the real scenario, performance of previous top-performing countermeasure systems had a significant degradation. The main reason for this phenomenon can be attributed to a simulation-to-real gap. In this work, the effect of sim-to-real gap is investigated on different datasets for replay attacks. Differences in the frequency domain between simulated and real datasets are investigated to cross the sim-to-real gap. On the basis of our previous work, different sub-band acoustic features have different capabilities in distinguishing spoof utterances from bonafide ones. To decrease the effect of sim-to-real gap and build a robust anti-spoofing system against the replay attacks, a cross-subband countermeasure is proposed in this work. Furthermore, we use visualized heatmap to explore the artefacts captured by model trained with cross-subband method. To verify the generalization capability of the cross-subband method on different datasets, several sets of comparative experiments were also done. The results show that our cross-subband countermeasure is robust to sim-to-real gap in the PA task, and the fusion model based on it is regarded as one of the top-performing antispoofing systems in the ASVspoof2021 Challenge.

doi: 10.21437/Odyssey.2022-18

Cite as: Lu, J., Zhang, Y., Wang, W., Zhang, P. (2022) Robust Cross-SubBand Countermeasure Against Replay Attacks. Proc. The Speaker and Language Recognition Workshop (Odyssey 2022), 126-132, doi: 10.21437/Odyssey.2022-18

  author={Jingze Lu and Yuxiang Zhang and Wenchao Wang and Pengyuan Zhang},
  title={{Robust Cross-SubBand Countermeasure Against Replay Attacks}},
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2022)},