用提示调优提升音频伪造检测在小数据下的适应性与效率
Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset
- 通过提示调优无缝融合主流模型，缓解源域与目标域差异
- 仅需少量目标数据即可有效适配，且不增加额外参数量
- 计算开销极低，适合资源受限场景下的实时检测
我们研究音频深度伪造检测（ADD）中的测试时领域自适应问题，解决三大挑战：(i) 源域与目标域之间的分布差异，(ii) 目标数据集规模有限，(iii) 高计算成本。提出一种基于提示调优的插件式ADD方法，可无缝集成于当前主流Transformer模型或其他微调方法中，有效缓解域间差异并提升目标数据上的性能（挑战(i)）。该方法因无需大量新增参数，可高效适应小规模目标数据集（挑战(ii)），同时显著降低计算开销，克服了大模型在ADD任务中常见的高资源消耗问题（挑战(iii)）。结果表明，在存在域差异的情况下，提示调优为实现高精度检测提供了低数据、低开销的可行路径。
原文摘要 · Abstract (English)
We study test-time domain adaptation for audio deepfake detection (ADD), addressing three challenges: (i) source-target domain gaps, (ii) limited target dataset size, and (iii) high computational costs. We propose an ADD method using prompt tuning in a plug-in style. It bridges domain gaps by integrating it seamlessly with state-of-the-art transformer models and/or with other fine-tuning methods, boosting their performance on target data (challenge (i)). In addition, our method can fit small target datasets because it does not require a large number of extra parameters (challenge (ii)). This feature also contributes to computational efficiency, countering the high computational costs typically associated with large-scale pre-trained models in ADD (challenge (iii)). We conclude that prompt tuning for ADD under domain gaps presents a promising avenue for enhancing accuracy with minimal target data and negligible extra computational burden.
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