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Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors
artificial-intelligencecomputer-vision-and-pattern-recognitioncryptography-and-securitymachine-learningsound
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 conditio
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