面向神经音频编解码器源解析的研究
Towards Neural Audio Codec Source Parsing
AI总结：
针对音频深度伪造开集归因无法刻画未知编解码器的缺陷，提出将源归因重构为NAC参数结构化回归的NACSP范式，及基于双曲几何的HYDRA框架，构建首个综合基准并取得最优结果。
摘要：
一类新型音频深度伪造——codecfakes（CFs，编解码器伪造）近期受到关注，它由后端利用神经音频编解码器（NACs）的音频语言模型合成。对此，学界已推出专用基准和定制化检测策略。随着领域发展，研究已从二分类检测转向源归因，包括开集归因——其目标是识别负责生成的NAC，并在推理阶段标记未见过的新型NAC。这种向源归因的转变提升了取证的可解释性和问责性。然而，开集归因仍存在根本局限：它虽能检测出某个NAC是陌生的，却无法描述或识别单个未见编解码器的特征，只会将这类输入归为通用的“未知项”，无法洞察其内部配置。这导致两大主要缺陷：对新型NAC的泛化能力有限，且无法区分NAC家族内部的细粒度差异。为弥补这些不足，我们提出神经音频编解码器源解析（NACSP）——这是一种范式转变，将CFs的源归因重构为对生成式NAC参数（如量化器、带宽和采样率）的结构化回归。我们将NACSP建模为预测这些NAC参数的多任务回归任务，并利用多种先进的语音预训练模型（PTMs）构建了首个综合基准。为此，我们提出HYDRA这一新型框架，它利用双曲几何从PTM表示中解耦复杂的潜在属性。通过在多个曲率感知双曲子空间上采用任务特定注意力机制，HYDRA实现了更优的多任务泛化能力。我们的大量实验表明，与在欧几里得空间中运行的基线方法相比，HYDRA在CFs基准数据集上取得了最优结果。
英文摘要：
A new class of audio deepfakes-codecfakes (CFs)-has recently caught attention, synthesized by Audio Language Models that leverage neural audio codecs (NACs) in the backend. In response, the community has introduced dedicated benchmarks and tailored detection strategies. As the field advances, efforts have moved beyond binary detection toward source attribution, including open-set attribution, which aims to identify the NAC responsible for generation and flag novel, unseen ones during inference. This shift toward source attribution improves forensic interpretability and accountability. However, open-set attribution remains fundamentally limited: while it can detect that a NAC is unfamiliar, it cannot characterize or identify individual unseen codecs. It treats such inputs as generic ``unknowns'', lacking insight into their internal configuration. This leads to major shortcomings: limited generalization to new NACs and inability to resolve fine-grained variations within NAC families. To address these gaps, we propose Neural Audio Codec Source Parsing (NACSP) - a paradigm shift that reframes source attribution for CFs as structured regression over generative NAC parameters such as quantizers, bandwidth, and sampling rate. We formulate NACSP as a multi-task regression task for predicting these NAC parameters and establish the first comprehensive benchmark using various state-of-the-art speech pre-trained models (PTMs). To this end, we propose HYDRA, a novel framework that leverages hyperbolic geometry to disentangle complex latent properties from PTM representations. By employing task-specific attention over multiple curvature-aware hyperbolic subspaces, HYDRA enables superior multi-task generalization. Our extensive experiments show HYDRA achieves top results on benchmark CFs datasets compared to baselines operating in Euclidean space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
