TY - GEN
T1 - DeFake
T2 - 23rd IEEE International Workshop on Multimedia Signal Processing, MMSP 2021
AU - Nagothu, Deeraj
AU - Xu, Ronghua
AU - Chen, Yu
AU - Blasch, Erik
AU - Aved, Alexander
N1 - Publisher Copyright: © 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Authenticity
KW - Deepfake Detection
KW - Electrical Network Frequency (ENF) Signals
KW - Environmental Fingerprint
KW - Proof-of-ENF (PoENF) Consensus
UR - https://www.scopus.com/pages/publications/85120902735
U2 - 10.1109/MMSP53017.2021.9733503
DO - 10.1109/MMSP53017.2021.9733503
M3 - Conference contribution
T3 - IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021
BT - IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 October 2021 through 8 October 2021
ER -