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This research addresses the critical bottleneck of deploying high-performance audio deepfake detectors on resource-constrained devices by introducing a task-aware framework for joint pruning and distillation. By transferring forgery-discriminative knowledge while preserving structural integrity under aggressive compression, the method reduces model size to 31.9M parameters with minimal accuracy loss, enabling practical on-device security solutions.
Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection
arXiv:2610.05264v1 Announce Type: cross Abstract: 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 critical structures under aggressive compression. Our framework reduces the model to 31.9M parameters with 6.3$\times$ FLOPs reduction, with an average performance drop of only 1.30\% across multiple datasets compared to the uncompressed baseline, demonstrating strong potential for on-device deployment.
Abstract: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 critical structures under aggressive compression. Our framework reduces the model to 31.9M parameters with 6.3$\times$ FLOPs reduction, with an average performance drop of only 1.30\% across multiple datasets compared to the uncompressed baseline, demonstrating strong potential for on-device deployment.
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2610.05264 [cs.SD]
(or arXiv:2610.05264v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2610.05264 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.21437/Interspeech.2026-1766 Focus to learn more DOI(s) linking to related resources
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