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.
翻译：近年来，生成式音频模型的进展使得高保真环境声音合成成为可能，这引发了人们对音频安全的严重担忧。因此，ESDD 2026挑战赛旨在解决在未见过的生成器（赛道1）和黑盒低资源检测（赛道2）条件下的环境声音深度伪造检测问题。我们提出了EnvSSLAM-FFN系统，该系统集成了一个冻结的SSLAM自监督编码器与一个轻量级的FFN后端。为了在严重数据不平衡条件下有效捕捉伪造痕迹，我们融合了来自第4至9层的中间SSLAM表征，并采用了类别加权的训练目标。实验结果表明，所提出的系统在两个赛道上均持续优于官方基线，分别取得了1.20%和1.05%的测试等错误率。
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