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CAFAD is proposed, a plug-and-play detection framework that combines multi-domain acoustic feature fusion and temporal pyramid matching for variable-length adversarial audio detection that achieves an average detection accuracy of 99.25%, with a false positive rate of 1.00% on benign samples.
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