基于声道物理动力学的语音伪造检测 Audio Physical Dynamics Inspired Deepfake Detection for Voice Authentication Systems

精读摘要

部署在网络边缘的声纹认证系统面临双重威胁：一是高仿真深度伪造合成攻击，二是分布式联邦学习中的控制面投毒。研究团队提出一个框架，把刻画声道动力学的可解释物理特征与自监督学习模块的表征融合，再用精简多层感知机处理，并耦合不确定性感知的边缘学习，从物理层面对抗语音伪造。 Voice authentication systems at the network edge face dual threats: sophisticated deepfake synthesis attacks and control-plane poisoning in federated learning. This framework fuses interpretable physics features modeling vocal tract dynamics with self-supervised learning representations, processed via a streamlined multilayer perceptron, coupled with uncertainty-aware edge learning.