DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication

• URL: http://arxiv.org/abs/2207.13070v1
• Date: Fri, 22 Jul 2022 01:22:11 GMT
• Title: DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication
• Authors: Deeraj Nagothu, Ronghua Xu, Yu Chen, Erik Blasch, Alexander Aved
• Abstract summary: DeFakePro is a consensus mechanism-based Deepfake detection technique in online video conferencing tools. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools.
• Score: 66.2466055910145
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 media in both audio and video channels.

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