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arXiv · 2512.20369

EnvSSLAM-FFN: Lightweight Layer-Fused System for ESDD 2026 Challenge

Xiaoxuan Guo·Hengyan Huang·Jiayi Zhou·Renhe Sun·Jian Liu·Haonan Cheng·Long Ye·Qin Zhang
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Abstract

Recent advances in generative audio models have enabled high-fidelity environmental sound synthesis, raising serious concerns for audio security. The ESDD 2026 Challenge therefore addresses environmental sound deepfake detection under unseen generators (Track 1) and black-box low-resource detection (Track 2) conditions. We propose EnvSSLAM-FFN, which integrates a frozen SSLAM self-supervised encoder with a lightweight FFN back-end. To effectively capture spoofing artifacts under severe data imbalance, we fuse intermediate SSLAM representations from layers 4-9 and adopt a class-weighted training objective. Experimental results show that the proposed system consistently outperforms the official baselines on both tracks, achieving Test Equal Error Rates (EERs) of 1.20% and 1.05%, respectively.

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Xiaoxuan Guo, Hengyan Huang, Jiayi Zhou, Renhe Sun, Jian Liu, Haonan Cheng, Long Ye, Qin Zhang. 2025-12-23. EnvSSLAM-FFN: Lightweight Layer-Fused System for ESDD 2026 Challenge. https://arxiv.org/abs/2512.20369

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