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RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection

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arxiv 2406.06086 v2 pith:BIRPWWUG submitted 2024-06-10 cs.SD eess.AS

Yujie Chen , Jiangyan Yi , Jun Xue , Chenglong Wang , Xiaohui Zhang , Shunbo Dong , Siding Zeng , Jianhua Tao
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Lv Zhao Cunhang Fan
This is my paper
classification cs.SDeess.AS
keywords audiobidirectionalinformationlong-rangecapturefakerawbmambashort-
verification ladder T0 review T1 audit T2 compute T3 formal
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Fake artefacts for discriminating between bonafide and fake audio can exist in both short- and long-range segments. Therefore, combining local and global feature information can effectively discriminate between bonafide and fake audio. This paper proposes an end-to-end bidirectional state space model, named RawBMamba, to capture both short- and long-range discriminative information for audio deepfake detection. Specifically, we use sinc Layer and multiple convolutional layers to capture short-range features, and then design a bidirectional Mamba to address Mamba's unidirectional modelling problem and further capture long-range feature information. Moreover, we develop a bidirectional fusion module to integrate embeddings, enhancing audio context representation and combining short- and long-range information. The results show that our proposed RawBMamba achieves a 34.1\% improvement over Rawformer on ASVspoof2021 LA dataset, and demonstrates competitive performance on other datasets.

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Cited by 5 Pith papers

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  1. Less is More: Modality-Decoupling for General AIGC Audio-Video Detection

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A decoupled audio-video AIGC detector that fuses independent audio and visual predictions at decision level ranks first in the DDL 2.0 general AIGC detection challenge with a final score of 0.8460.

  2. Probing Speaker Identity Sensitivity in Audio Deepfake Detectors

    cs.SD 2026-07 conditional novelty 6.0 of 10

    A new Identity Sensitivity Score flags misclassified audio deepfake detections with AUC up to 0.954, but its claim to isolate speaker-identity behavior from plain confidence is not yet controlled.

  3. SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection

    cs.SD 2025-11 conditional novelty 6.0 of 10

    SONAR improves audio deepfake detection by explicitly aligning low- and high-frequency representations for real speech and repelling them for fakes, setting new benchmark EERs on ASVspoof 2021 and in-the-wild data.

  4. Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems

    cs.CR 2025-07 conditional novelty 3.0 of 10

    A systematic review of deepfake detection finds a pervasive lack of adversarial robustness evaluation across all modalities and calls for resilient, modality-agnostic detectors.

  5. A Survey of Mamba

    cs.LG 2024-08 unverdicted novelty 2.0 of 10

    The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.

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