Audio Physical Dynamics Inspired Deepfake Detection for Voice Authentication Systems
Quick summary
arXiv:2512.06040v2 Announce Type: replace-cross Abstract: 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 audio physical dynamics 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 streamlined Multi-Layer Perceptron
Key takeaways
- arXiv:2512.06040v2 Announce Type: replace-cross Abstract: 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 audio physical dynamics 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.
Why it matters
“Audio Physical Dynamics Inspired Deepfake Detection for Voice Authentication Systems” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.
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