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arXiv · 2606.07494

Mitigating Proxy-to-Wild Domain Gap in Deepfake Speech

Xuanjun Chen·Yun-Shing Wu·Wei-Chung Lu·Claire Lin·Haibin Wu·Hung-yi Lee·Jyh-Shing Roger Jang
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Abstract

Recent neural audio codec-based speech generation (CodecFake) produces highly realistic audio, posing a challenge to existing deepfake countermeasure models. While using codec resynthesized speech (CoRS) as proxy data improves performance, it often suffers from limited generalization. We propose Domain-Shift Feature Augmentation (DSFA), which simulates "in-the-wild" variations by transforming deterministic feature statistics into stochastic distributions during fine-tuning. To evaluate generalization, we further introduce Codec-based Speech Generation Extension Evaluation (CoSG ExtEval) dataset, a more challenging extension of the CoSG Eval (from CodecFake+) dataset, featuring 40 unseen generative models and long-form audio. Experimental results demonstrate that combining a post-trained SSL backbone with DSFA effectively narrows the proxy-to-wild domain gap. This approach achieves state-of-the-art performance across diverse CodecFake attacks in both CoSG Eval and CoSG ExtEval.

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Xuanjun Chen, Yun-Shing Wu, Wei-Chung Lu, Claire Lin, Haibin Wu, Hung-yi Lee, Jyh-Shing Roger Jang. 2026-06-05. Mitigating Proxy-to-Wild Domain Gap in Deepfake Speech. https://arxiv.org/abs/2606.07494

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