DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication
Deeraj Nagothu, Ronghua Xu, Yu Chen, Erik Blasch, Alexander Aved
TL;DR
DeFakePro introduces a decentralized Deepfake detection method using ENF signal analysis and a novel Proof-of-ENF consensus algorithm to verify media authenticity in online video conferencing.
Contribution
It presents a new decentralized detection approach leveraging ENF signals and a consensus mechanism for authenticating media in real-time conferencing environments.
Findings
Effective detection of Deepfake videos using ENF signal similarity.
Decentralized consensus mechanism enhances robustness against malicious actors.
Applicable to both audio and video streams in conferencing settings.
Abstract
Advancements in generative models, like Deepfake allows users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based Deepfake detection technique in online video conferencing tools. Leveraging Electrical Network Frequency (ENF), an environmental fingerprint embedded in digital media recording, affords a consensus mechanism design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deep fake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming…
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