GLAD: Global-Local Adaptive Detector for Robust Speech Deepfake Detection
Recent advances in AI-based speech synthesis have enabled highly realistic speech, increasing the importance of speech deepfake detection (SDD) in preventing misuse. While mainstream Self-Supervised Learning (SSL)-based detectors achieve strong performance, they suffer from poor generalization to unseen domains and often overlook fine-grained signal artifacts due to a bias towards global semantic consistency. In this paper, we conduct the first detailed empirical and visual analysis to validate these limitations explicitly. Our investigation reveals two critical architectural vulnerabilities: (1) a systemic failure to capture localized spoofing traces, and (2) a severe lack of adaptabilit...
| Authors | Zelin Zhao, Guanjie Huang, Danny Hin Kwok Tsang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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