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arXiv · 2604.26465

Diffusion Reconstruction towards Generalizable Audio Deepfake Detection

Bo Cheng·Songjun Cao·Xiaoming Zhang·Jie Chen·Long Ma·Fei Chen
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Abstract

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.

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Bo Cheng, Songjun Cao, Xiaoming Zhang, Jie Chen, Long Ma, Fei Chen. 2026-04-29. Diffusion Reconstruction towards Generalizable Audio Deepfake Detection. https://arxiv.org/abs/2604.26465

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