A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis
arXiv:2606.08669v1 Announce Type: cross Abstract: Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To investigate these unpredictable fluctuations, we conduct a comprehensive benchmark of four self-supervised learning feature extractors paired with four back-end classifiers. We compare the hierarchical local feature extraction of ResNet with the global sequence and relational modeling of attention and graph-based back-ends. Through multi-corpus training across three scenarios and six evaluation data
The proliferation of voice biometric systems necessitates robust spoofing detection, a need amplified by advancements in AI-driven voice synthesis and manipulation.
Improved voice spoofing detection is critical for securing biometric authentication, preventing fraud, and maintaining trust in automated systems across various sectors.
This research provides a more consistent and robust evaluation framework for spoofing detection models, which could lead to more reliable and deployable defensive AI solutions.
- · Cybersecurity industry
- · Financial institutions
- · Government agencies
- · Voice biometric system developers
- · Voice spoofing attack developers
- · Fraudsters
Increased difficulty for malicious actors to bypass voice authentication systems.
Enhanced public and institutional confidence in voice-based security measures, leading to broader adoption.
A potential arms race between increasingly sophisticated spoofing techniques and advanced detection models, driving continuous innovation in AI security.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG