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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.26465 |
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| _version_ | 1866909000670380032 |
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| author | Cheng, Bo Cao, Songjun Zhang, Xiaoming Chen, Jie Ma, Long Chen, Fei |
| author_facet | Cheng, Bo Cao, Songjun Zhang, Xiaoming Chen, Jie Ma, Long Chen, Fei |
| contents | Achieving robust generalization against unseen attacks remains a challenge in Audio Deepfake Detection (ADD), driven by the rapid evolution of generative models. To address this, we propose a framework centered on hard sample classification. The core idea is that a model capable of distinguishing challenging hard samples is inherently equipped to handle simpler cases effectively. We investigate multiple reconstruction paradigms, identifying the diffusion-based method as optimal for generating hard samples. Furthermore, we leverage multi-layer feature aggregation and introduce a Regularization-Assisted Contrastive Learning (RACL) objective to enhance generalizability. Experiments demonstrate the superior generalization of our approach, with our best model achieving a significant reduction in the average Equal Error Rate (EER) compared to the baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26465 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Diffusion Reconstruction towards Generalizable Audio Deepfake Detection Cheng, Bo Cao, Songjun Zhang, Xiaoming Chen, Jie Ma, Long Chen, Fei Sound Achieving robust generalization against unseen attacks remains a challenge in Audio Deepfake Detection (ADD), driven by the rapid evolution of generative models. To address this, we propose a framework centered on hard sample classification. The core idea is that a model capable of distinguishing challenging hard samples is inherently equipped to handle simpler cases effectively. We investigate multiple reconstruction paradigms, identifying the diffusion-based method as optimal for generating hard samples. Furthermore, we leverage multi-layer feature aggregation and introduce a Regularization-Assisted Contrastive Learning (RACL) objective to enhance generalizability. Experiments demonstrate the superior generalization of our approach, with our best model achieving a significant reduction in the average Equal Error Rate (EER) compared to the baseline. |
| title | Diffusion Reconstruction towards Generalizable Audio Deepfake Detection |
| topic | Sound |
| url | https://arxiv.org/abs/2604.26465 |