Comparing Self-Supervised and Domain-Invariant Features for Cross-Domain Voice Phishing Detection

2026-09-07

AI Summary

研究比较了领域不变韵律特征和自监督表示（HuBERT、wav2vec2.0）在跨域语音钓鱼检测中的表现，解决了数据稀缺及模型轻量化需求的问题。

Abstract

Voice phishing detection faces three critical challenges: real criminal recordings are unavailable due to privacy constraints; when available, only a handful of samples exist, insufficient for fine-tuning; and lightweight acoustic-only detection is needed as an alternative to large self-supervised models. We compare domain-invariant prosodic features and self-supervised representations (HuBERT, wav2vec2.0) through cross-domain evaluation-training on scenario-based actor recordings and testing on authentic criminal calls. Domain-invariant prosodic features achieve 69.5% F1 zero-shot and 71.0% with 5-shot learning. HuBERT achieves highest performance (94.2% F1, 5-shot), while wav2vec2.0 exhibits a precision-oriented detection profile (90.2% F1 with 99.4% precision, 5-shot). These findings reveal fundamental trade-offs: domain-invariant features enable zero-shot deployment when no real data exists, while SSL methods achieve higher performance but require real samples and compute.

Problem

• voice phishing detection
• privacy constraints
• cross-domain evaluation
• domain-invariant features
• self-supervised representations

Innovation

• domain-invariant prosodic features
• self-supervised representations
• cross-domain evaluation
• zero-shot learning
• few-shot learning