Comparative Analysis of ASV Spoofing Countermeasures: Evaluating Res2Net-Based Approaches
Minjiao Yang, Kangfeng Zheng, Xiujuan Wang, Yudao Sun, Zhe Chen
Abstract
Popular topics in the field of countermeasures include feature engineering and neural-network-based models, which involve neural network architectures and loss criteria. This study focuses on Res2Net and its variant models to examine the impact of model generalization on countermeasure performance in the ASVspoof 2019 logical access and physical access scenarios. Results reveal that while Res2Net exhibits superior generalization compared to its variants, the most effective countermeasure combines both feature engineering and model optimization. The proposed dynamic modulated-Res2Net utilizes channel-wise soft attention to recalibrate feature maps, offering adaptive adjustments to spoofing cues of varying scales. Evaluation on the logical access dataset demonstrates dynamic modulated-Res2Net's relative improvement of over 38% compared to Res2Net. Furthermore, we exploit low-frequency features and combine them with dynamic modulated-Res2Net to achieve in an equal error rate of 1.21% under logical access and 0.41% under physical access, establishing our proposed dynamic modulated-Res2Net as one of the top-performing single systems. Additionally, we compare the best countermeasures in different scenarios, highlighting the ongoing challenge of achieving generalization.