论文标题
DefakePro：使用ENF身份验证分散的深击攻击检测
DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication
论文作者
论文摘要
像DeepFake这样的生成模型的进步使用户可以模仿目标人并操纵在线互动。人们已经认识到，虚假信息可能会在社会中引起干扰并破坏信任的基础。本文介绍了DeFakePro，这是在线视频会议工具中基于分散的共识机制的深层捕获技术。利用电网频率（ENF）是一种嵌入数字媒体记录中的环境指纹，提供了一种共识机制设计，称为ENF证明（POENF）算法。在POENF算法中使用ENF信号波动的相似性来验证会议工具中广播的媒体。通过利用与恶意参与者的视频会议设置，向其他参与者广播深层的假视频录制，DeFakePro系统可以在音频和视频频道中验证传入媒体的真实性。
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.
