Year: 2022
Type: article
Abstract: Advancements in generative models, such as deepfake, allow 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 deepfake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.
Source: IT Professional
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G3939628135
OpenAlex ID: W4288099671