Towards Generalized Source Tracing for Codec-Based Deepfake Speech

• URL: http://arxiv.org/abs/2506.07294v2
• Date: Tue, 10 Jun 2025 03:26:40 GMT
• Title: Towards Generalized Source Tracing for Codec-Based Deepfake Speech
• Authors: Xuanjun Chen, I-Ming Lin, Lin Zhang, Haibin Wu, Hung-yi Lee, Jyh-Shing Roger Jang,
• Abstract summary: We introduce the Semantic-Acoustic Source Tracing Network (SASTNet), which jointly leverages Whisper for semantic feature encoding and Wav2vec2 with AudioMAE for acoustic feature encoding.<n>Our proposed SASTNet achieves state-of-the-art performance on the CoSG test set of the CodecFake+ dataset, demonstrating its effectiveness for reliable source tracing.
• Score: 52.68106957822706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent attempts at source tracing for codec-based deepfake speech (CodecFake), generated by neural audio codec-based speech generation (CoSG) models, have exhibited suboptimal performance. However, how to train source tracing models using simulated CoSG data while maintaining strong performance on real CoSG-generated audio remains an open challenge. In this paper, we show that models trained solely on codec-resynthesized data tend to overfit to non-speech regions and struggle to generalize to unseen content. To mitigate these challenges, we introduce the Semantic-Acoustic Source Tracing Network (SASTNet), which jointly leverages Whisper for semantic feature encoding and Wav2vec2 with AudioMAE for acoustic feature encoding. Our proposed SASTNet achieves state-of-the-art performance on the CoSG test set of the CodecFake+ dataset, demonstrating its effectiveness for reliable source tracing.

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