Abstract
Partial deepfake speech detection requires identifying manipulated regions that may occur within short temporal portions of an otherwise bona fide utterance, making the task particularly challenging for conventional utterance-level classifiers. We propose a split-and-conquer framework that decomposes the problem into two stages: boundary detection and segment-level classification. A dedicated boundary detector first identifies temporal transition points, allowing the audio signal to be divided into segments that are expected to contain acoustically consistent content. Each resulting segment is then evaluated independently to determine whether it corresponds to bona fide or fake speech. This formulation simplifies the learning objective by explicitly separating temporal localization from authenticity assessment, allowing each component to focus on a well-defined task. To further improve robustness, we introduce a reflection-based multi-length training strategy that converts variable-duration segments into several fixed input lengths, producing diverse feature-space representations. Each stage is trained using multiple configurations with different feature extractors and augmentation strategies, and their complementary predictions are fused to obtain improved final models. Experiments on the PartialSpoof benchmark demonstrate state-of-the-art performance across multiple temporal resolutions as well as at the utterance level, with substantial improvements in the accurate detection and localization of spoofed regions. In addition, the proposed method achieves state-of-the-art performance on the Half-Truth dataset, further confirming the robustness and generalization capability of the framework.
Abstract (translated)
部分深度伪造语音检测需要识别在原本真实的语音中可能出现的短时 manipulated 区域，这使得传统的语句级分类器面临特别大的挑战。我们提出一个分而治之框架，将问题分解为两个阶段：边界检测和片段级分类。首先，一个专用的边界检测器识别时间过渡点，从而将音频信号分割为预期包含声学一致内容的片段。随后，对每个生成的片段进行独立评估，以确定其对应真实语音还是伪造语音。这种形式通过明确分离时间定位与真实性评估来简化学习目标，使每个组件都能专注于定义明确的任务。为进一步提升鲁棒性，我们引入了基于镜像的多长度训练策略，将可变时长的片段转换为多个固定输入长度，产生多样化的特征空间表示。每个阶段均采用多种配置进行训练，结合不同的特征提取器和增强策略，并通过融合其互补预测来获得改进的最终模型。在 PartialSpoof 基准测试上的实验表明，该方法在多种时间分辨率及语句级别均达到最先进性能，在伪造区域的准确检测与定位方面取得显著提升。此外，所提方法在 Half-Truth 数据集上也实现了最先进性能，进一步验证了该框架的鲁棒性与泛化能力。
URL
https://arxiv.org/abs/2604.02913
