AI RESEARCH
AFSS: Artifact-Focused Self-Synthesis for Mitigating Bias in Audio Deepfake Detection
arXiv CS.AI
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ArXi:2603.26856v1 Announce Type: cross 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.