SZU-AFS Antispoofing System for the ASVspoof 5 Challenge

TL;DR

This paper introduces the SZU-AFS anti-spoofing system for the ASVspoof 5 Challenge, utilizing advanced data augmentation and a co-enhancement strategy to improve spoof detection accuracy.

Contribution

The paper proposes a novel anti-spoofing system combining multiple data augmentation policies and a gradient norm aware minimization strategy for enhanced generalization.

Findings

Achieved a minDCF of 0.115 on evaluation set

Attained an EER of 4.04% on evaluation set

Demonstrated effectiveness of co-enhancement strategy

Abstract

This paper presents the SZU-AFS anti-spoofing system, designed for Track 1 of the ASVspoof 5 Challenge under open conditions. The system is built with four stages: selecting a baseline model, exploring effective data augmentation (DA) methods for fine-tuning, applying a co-enhancement strategy based on gradient norm aware minimization (GAM) for secondary fine-tuning, and fusing logits scores from the two best-performing fine-tuned models. The system utilizes the Wav2Vec2 front-end feature extractor and the AASIST back-end classifier as the baseline model. During model fine-tuning, three distinct DA policies have been investigated: single-DA, random-DA, and cascade-DA. Moreover, the employed GAM-based co-enhancement strategy, designed to fine-tune the augmented model at both data and optimizer levels, helps the Adam optimizer find flatter minima, thereby boosting model generalization. Overall, the final fusion system achieves a minDCF of 0.115 and an EER of 4.04% on the evaluation set.