Probabilistic Verification of Voice Anti-Spoofing Models
Recent advances in generative models have amplified the risk of malicious misuse of speech synthesis technologies, enabling adversaries to impersonate target speakers and access sensitive resources. Although speech deepfake detection has progressed rapidly, most existing countermeasures lack formal robustness guarantees or fail to generalize to unseen generation techniques. We propose PV-VASM, a probabilistic framework for verifying the robustness of voice anti-spoofing models (VASMs). PV-VASM estimates the probability of misclassification under text-to-speech (TTS), voice cloning (VC), and parametric signal transformations. The approach is model-agnostic and enables robustness verificati...
| Authors | Evgeny Kushnir, Alexandr Kozodaev, Dmitrii Korzh et al. |
|---|---|
| Primary category | cs.SD |
| Published | 11 March 2026 |
| Last updated at source | 12 March 2026 |
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