Deepfake Audio Detection Using Self-supervised Fusion Representations
This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level deepfake detection using the CompSpoofV2 dataset, where speech and environmental sounds may be independently manipulated. To address this challenge, a dual-branch deepfake detection framework is proposed to jointly model speech and environmental contextual representations from input audio. Two pretrained models, XLS-R for speech and BEATs for environmental sound, are used to extract complementary contextual representations. A Matching Head is introduced to model representation differences through statistical normalization and representation...
| Authors | Khalid Zaman, Qixuan Huang, Muhammad Uzair et al. |
|---|---|
| Primary category | cs.SD |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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