DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication
This addresses the issue of disinformation and trust erosion in society caused by DeepFakes, specifically for users of online video conferencing tools, but it appears incremental as it builds on existing ENF-based techniques.
The paper tackles the problem of detecting DeepFake attacks in online video conferencing by proposing DeFakePro, a decentralized system that uses Electrical Network Frequency (ENF) signals for authentication, resulting in a method to verify media authenticity in both audio and video channels.
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.