arXiv cs.AI· Ajinkya Kulkarni, Sandipana Dowerah, Atharva Kulkarni, Tanel Alum\"ae, Mathew Magimai Doss·· 4 小时前AI 评分36
RAPTOR 受控研究：紧凑型 SSL 骨干网络对音频深度伪造检测是否重要
Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR
AI 导读
arXiv 论文提出 RAPTOR（Representation Aware Pairwise-gated Transformer for Out-of-domain Recognition），在统一的 pairwise-gated 融合检测器中对比 HuBERT 与 WavLM 紧凑 SSL 骨干，跨 14 个跨域基准评测。
正文 · 原文
Abstract:Self-supervised learning (SSL) underpins modern audio deepfake detection, yet most prior work centers on a single large wav2vec2-XLSR backbone, leaving compact under studied. We present RAPTOR, Representation Aware Pairwise-gated Transformer for Out-of-domain Recognition a controlled study of compact SSL backbones from the HuBERT and WavLM within a unified pairwise-gated fusion detector, evaluated across 14 cross-domain benchmarks. We show that multilingual HuBERT pre-training is the primary driver of cross-domain robustness, enabling 100M models to match larger and commercial systems. Beyond EER, we introduce a test-time augmentation protocol with perturbation-based aleatoric uncertainty to expose calibration differences invisible to standard metrics: WavLM variants exhibit overconfident miscalibration under perturbation, whereas iterative mHuBERT remains stable. These findings indicate that SSL pre-training trajectory, not model scale, drives reliable audio deepfake detection.
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.06164 [cs.SD] |
| (or arXiv:2603.06164v2 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2603.06164 arXiv-issued DOI via DataCite |
Submission history
From: Ajinkya Kulkarni [view email]
[v1]
Fri, 6 Mar 2026 11:16:55 UTC (315 KB)
[v2]
Sun, 25 Oct 2026 14:58:36 UTC (315 KB)
来源：arXiv cs.AI · arxiv.org