Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors
Deepfake speech detectors are typically evaluated as binary classifiers on fixed benchmarks, but deployed systems face evolving attacks, codecs, speakers, and recording conditions, with labels unavailable during operation. This makes it difficult to know when a detector operates in familiar conditions and can be relied on. We propose an unsupervised drift monitor that operates on the internal embeddings of a deployed detector. The monitor compares each input against bona fide and spoof references using regularized Mahalanobis distances. These distances define trusted, ambiguous, and novel regions of the detector embedding space, allowing unfamiliar inputs to be routed for review. We evalu...
| Authors | Lukáš Prokeš, Vojtěch Staněk, Anton Firc et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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