基于神经音频编解码器分类法的编解码器深度伪造源追踪
Codec-Based Deepfake Source Tracing via Neural Audio Codec Taxonomy
AI总结：
针对神经音频编解码器语音生成模型产生的深度伪造音频，提出基于神经音频编解码器分类法的源追踪方法，在CodecFake+数据集上验证了可行性并指出挑战。
摘要：
神经音频编解码器语音生成（CoSG）模型的最新进展已产生出极其逼真的音频深度伪造。我们将由CoSG系统生成的深度伪造语音称为基于编解码器的深度伪造，即CodecFake。尽管现有关于CodecFake的反欺骗研究主要集中于验证音频样本的真实性，但几乎未关注追踪生成这些深度伪造所用的CoSG。在CodecFake生成过程中，语音到单元编码、离散单元建模以及单元到语音解码等过程从根本上依赖于神经音频编解码器。受此启发，我们通过神经音频编解码器分类法引入CodecFake源追踪，该方法剖析神经音频编解码器以追踪CoSG。我们在CodecFake+数据集上的实验结果，为CodecFake源追踪的可行性提供了有前景的初步证据，同时也凸显了若干值得进一步研究的挑战。
英文摘要：
Recent advances in neural audio codec-based speech generation (CoSG) models have produced remarkably realistic audio deepfakes. We refer to deepfake speech generated by CoSG systems as codec-based deepfake, or CodecFake. Although existing anti-spoofing research on CodecFake predominantly focuses on verifying the authenticity of audio samples, almost no attention was given to tracing the CoSG used in generating these deepfakes. In CodecFake generation, processes such as speech-to-unit encoding, discrete unit modeling, and unit-to-speech decoding are fundamentally based on neural audio codecs. Motivated by this, we introduce source tracing for CodecFake via neural audio codec taxonomy, which dissects neural audio codecs to trace CoSG. Our experimental results on the CodecFake+ dataset provide promising initial evidence for the feasibility of CodecFake source tracing while also highlighting several challenges that warrant further investigation.
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