Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative

TL;DR

Fake-Mamba introduces a real-time speech deepfake detection method using bidirectional Mamba as a self-attention alternative, achieving high accuracy and efficiency across multiple benchmarks.

Contribution

It proposes a novel framework integrating bidirectional Mamba with XLSR for effective, real-time synthetic speech detection, outperforming existing models.

Findings

Achieves state-of-the-art EER on multiple benchmarks

Maintains real-time inference across utterance lengths

Demonstrates strong generalization and practical viability

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.