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.
翻译:Deepfake等基因化模型的进步使用户能够模仿目标人物并操纵在线互动,人们认识到,假信息可能会对社会造成干扰并破坏信任的基础。这篇文章介绍了DeFakePro,这是在网上电视会议工具中以分散的共识机制为基础的深假检测技术。利用数码媒体记录中嵌入的环境指纹电子网络频率(ENF)提供了一种名为“EnF证据算法(PENF)”的协商一致机制设计。在PoENF算法中使用了ENF信号波动的相似性,以验证在会议工具中广播的媒体。通过与恶意参与者的视频会议设置向其他参与者播放深层的假录像,DeFakePro系统核查了新进入媒体的音像频道的真实性。