Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention’s Alternative
2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , 1–8 , 2025
Abstract
Advances in speech synthesis intensify security threats, motivating real-time deepfake detection research. We investigate whether bidirectional Mamba can serve as a competitive alternative to Self-Attention in detecting synthetic speech. Our solution, Fake-Mamba, integrates an XLSR front-end with bidirectional Mamba to capture both local and global artifacts. Our core innovation introduces three efficient encoders: TransBiMamba, ConBiMamba, and PN-BiMamba. Leveraging XLSR’s rich linguistic representations, PN-BiMamba can effectively capture the subtle cues of synthetic speech. Evaluated on ASVspoof 21 LA, 21 DF, and In-The-Wild benchmarks, Fake-Mamba achieves 0.97%, 1.74%, and 5.85% EER, respectively, representing substantial relative gains over SOTA models XLSR-Conformer and XLSR-Mamba. The framework maintains real-time inference across utterance lengths, demonstrating strong generalization and practical viability. The code is available at https://github.com/xuanxixi/Fake-Mamba.
BibTeX
@inproceedings{xuan2025fakemamba,
title = {Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative},
author = {Xuan, Xi and Zhu, Zimo and Zhang, Wenxin and Lin, Yi-Cheng and Kinnunen, Tomi},
booktitle = {2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
year = {2025},
pages = {1--8},
doi = {10.1109/ASRU65441.2025.11434679},
}