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Interpretable Spectrum Transformation Attacks to Speaker Recognition
Unlike existing approaches that operate voices in the time domain, the proposed framework operates voices in the time-frequency domain, which improves the interpretability, transferability, and imperceptibility of the attack.
Symmetric Saliency-based Adversarial Attack To Speaker Identification
Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge.
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