DeFake: Decentralized ENF-Consensus Based DeepFake Detection in Video Conferencing
Deeraj Nagothu, Ronghua Xu, Yu Chen, Erik Blasch, Alexander J. Aved · 2021
Modern video conferencing technologies provide state-of-the-art end-to-end encryption models but do not verify the authenticity of the media broadcast, where the verification of the media is left to the end-users. A perpetrator can forge the video or audio streams using replay attacks or deepfake attacks to manipulate the real-time perception of transcribed events. Leveraging Electrical Network Frequency (ENF) signals as an environmental fingerprint, this paper proposes a distributed consensus network-based audio authentication scheme named DeFake - Decentralized ENF-consensus based deepFake detection, which detects multimedia manipulations in real-time. Since the fluctuations in an ENF signal are of a distributed and random nature, a novel Proof-of-ENF (PoENF) algorithm can guarantee byzantine resistant deepfakes detection on audio streams with minimal computational resources. By utilizing audio conferencing or audio editing applications as the frontend software service, the DeFake solution can effectively and efficiently verify the authenticity of the recorded video clip.