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arXiv · 2603.26856

AFSS: Artifact-Focused Self-Synthesis for Mitigating Bias in Audio Deepfake Detection

Hai-Son Nguyen-Le·Hung-Cuong Nguyen-Thanh·Nhien-An Le-Khac·Dinh-Thuc Nguyen·Hong-Hanh Nguyen-Le
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Abstract

The rapid advancement of generative models has enabled highly realistic audio deepfakes, yet current detectors suffer from a critical bias problem, leading to poor generalization across unseen datasets. This paper proposes Artifact-Focused Self-Synthesis (AFSS), a method designed to mitigate this bias by generating pseudo-fake samples from real audio via two mechanisms: self-conversion and self-reconstruction. The core insight of AFSS lies in enforcing same-speaker constraints, ensuring that real and pseudo-fake samples share identical speaker identity and semantic content. This forces the detector to focus exclusively on generation artifacts rather than irrelevant confounding factors. Furthermore, we introduce a learnable reweighting loss to dynamically emphasize synthetic samples during training. Extensive experiments across 7 datasets demonstrate that AFSS achieves state-of-the-art performance with an average EER of 5.45\%, including a significant reduction to 1.23\% on WaveFake and 2.70\% on In-the-Wild, all while eliminating the dependency on pre-collected fake datasets. Our code is publicly available at https://github.com/NguyenLeHaiSonGit/AFSS.

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Hai-Son Nguyen-Le, Hung-Cuong Nguyen-Thanh, Nhien-An Le-Khac, Dinh-Thuc Nguyen, Hong-Hanh Nguyen-Le. 2026-03-27. AFSS: Artifact-Focused Self-Synthesis for Mitigating Bias in Audio Deepfake Detection. https://arxiv.org/abs/2603.26856

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