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arXiv · 2608.29021

Beyond Speech: Dual-Domain SSL Fusion for Unified All-Type Audio Deepfake Detection

Cunhang Fan·Junqin Cao·Tian Gao·Zhipeng Xie·Jun Xue·Zhao Lv·Xin Fang
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Abstract

Unified all-type audio deepfake detection aims to determine whether an input clip is real or fake when its audio type may be speech, environmental sound, singing voice, or music. Existing speech-centric or type-dependent solutions are insufficient for this setting because the test-time audio type is unknown, while the required output is still a single binary decision. To address these issues, this paper proposes a dual-domain SSL fusion method that maps heterogeneous audio into a shared binary authenticity space. EAT-large and wav2vec 2.0 XLS-R-300M are used as complementary SSL feature sources, providing broad acoustic and event-level representations as well as waveform-level, vocal, and speech-sensitive representations. Layer-wise weighted fusion integrates multi-level artifacts from different transformer depths, while token-level fusion forms a unified feature pool without enforcing frame-level alignment between the two SSL streams. The fused tokens are summarized by multi-head attentive statistics pooling and classified with a binary MLP head. With conservative speech refinement applied on top of this unified core detector, the submitted system achieves 95.58% Macro-F1 on the AT-ADD Track 2 evaluation set and ranks second in the challenge.

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BibTeXRIS

Cunhang Fan, Junqin Cao, Tian Gao, Zhipeng Xie, Jun Xue, Zhao Lv, Xin Fang. 2026-08-29. Beyond Speech: Dual-Domain SSL Fusion for Unified All-Type Audio Deepfake Detection. https://arxiv.org/abs/2608.29021

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