most citedPrompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset

3 citations · 3 across the 1 of their papers we have counts for

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cs.SD2024★ 3 cited

Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset

Hideyuki Oiso, Yuto Matsunaga, Kazuya Kakizaki +1

We study test-time domain adaptation for audio deepfake detection (ADD), addressing three challenges: (i) source-target domain gaps, (ii) limited target dataset size, and (iii) hig…