Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection
Advances in speech synthesis have made deepfake speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve state-of-the-art performance, their computational demands (typically 300M+ parameters) prevent deployment on resource-constrained devices. Existing compression methods, designed mainly for content-centric tasks, struggle to maintain competitive performance when directly adapted to deepfake detection. We propose a Task-Aware Joint Pruning and Distillation framework that combines cross-domain knowledge distillation with movement-guided structured pruning to transfer forgery-discriminative knowledge and preserve cri...
| Authors | Miao He, Peng Cheng, Zhongjie Ba et al. |
|---|---|
| Primary category | cs.SD |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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