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Singing deepfakes are a surprisingly effective attack vector, and this paper introduces a new benchmark and detection method to address the resulting security gap.
Even without retraining, a simple dual-system approach can significantly boost the performance of self-supervised talking head forgery detectors by refining the ordering of uncertain samples.
