ML Security PapersPhysics-Guided Deepfake Detection for Voice Authentication Systems
Alireza Mohammadi, Keshav Sood, Dhananjay Thiruvady et al. · Deakin University
Defends voice authentication against audio deepfakes and FL poisoning via physics-guided features with Bayesian uncertainty screening
Voice authentication systems deployed at the network edge face dual threats: a) sophisticated deepfake synthesis attacks and b) control-plane poisoning in distributed federated learning protocols. We present a framework coupling physics-guided deepfake detection with uncertainty-aware in edge learning. The framework fuses interpretable physics features modeling vocal tract dynamics with representations coming from a self-supervised learning module. The representations are then processed via a Multi-Modal Ensemble Architecture, followed by a Bayesian ensemble providing uncertainty estimates. Incorporating physics-based characteristics evaluations and uncertainty estimates of audio samples allows our proposed framework to remain robust to both advanced deepfake attacks and sophisticated control-plane poisoning, addressing the complete threat model for networked voice authentication.