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Human listeners misclassify genuine audio as fake 77% of the time, revealing a critical vulnerability in our ability to discern deepfake speech.
Deepfake speech detection may achieve sub-1% error rates in controlled settings, but real-world performance falters dramatically due to unforeseen challenges.
Global anchoring outperforms pairwise verification in synthetic speech source tracing, revealing hidden pitfalls in the latter's approach to metric learning.
Achieving a 20.7% reduction in compute while enhancing anti-spoofing robustness could redefine deployment strategies in self-supervised learning.
Training with speaker references might seem essential, but RAT shows that models can excel in deepfake detection even when those references are absent during inference.
Fairness assessments of deepfake speech detectors are fundamentally flawed due to a lack of demographic metadata in most datasets, limiting meaningful subgroup analysis.
Deepfake speech detectors reveal surprising reliance on distinct audio cues, challenging assumptions about their decision-making processes.
You can halve the size of your deepfake detection ensemble without sacrificing accuracy by using evolutionary multi-objective optimization to select and weight individual detectors.
