DeFakePro: Decentralized Deepfake Attacks Detection Using ENF Authentication
DeFakePro: Decentralized Deepfake Attacks Detection Using ENF Authentication
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DOI：
10.1109/mitp.2022.3172653
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发表时间：
2022-07
期刊：
影响因子：
2.6
通讯作者：
Deeraj Nagothu;Ronghua Xu;Yu Chen;E. Blasch;Alexander J. Aved
中科院分区：
文献类型：
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作者：
Deeraj Nagothu;Ronghua Xu;Yu Chen;E. Blasch;Alexander J. Aved
生成模型的进步，如deepfake，允许用户模仿目标人物并操纵在线互动。人们已经认识到，虚假信息可能会引起社会动荡，破坏信任的基础。本文介绍了DeFakePro，一种基于分布式共识机制的深度假检测技术，用于在线视频会议工具。利用电子网络频率（ENF），嵌入在数字媒体记录中的环境指纹提供了一种称为ENF证明（PoENF）算法的共识机制设计。PoENF算法利用ENF信号波动的相似性对会议工具中播放的媒体进行认证。通过利用带有恶意参与者的视频会议设置向其他参与者广播深度伪造的视频记录，DeFakePro系统在音频和视频通道中验证传入媒体的真实性。
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.