DeFake: Decentralized ENF-Consensus Based DeepFake Detection in Video Conferencing
DeFake: Decentralized ENF-Consensus Based DeepFake Detection in Video Conferencing
复制标题
DeFake：视频会议中基于去中心化 ENF 共识的 DeepFake 检测
DOI：
10.1109/mmsp53017.2021.9733503
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Alexander J. Aved
中科院分区：
文献类型：
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作者：
Deeraj Nagothu;Ronghua Xu;Yu Chen;Erik Blasch;Alexander J. Aved
现代视频会议技术提供最先进的端到端加密模型，但不验证媒体广播的真实性，其中媒体的验证留给最终用户。犯罪者可以使用重放攻击或deepfake攻击来伪造视频或音频流，以操纵转录事件的实时感知。利用电网络频率（ENF）信号作为环境指纹，本文提出了一种基于分布式共识网络的音频认证方案DeFake - Decentralized ENF-consensus based deepFake detection，该方案实时检测多媒体操作。由于ENF信号中的波动具有分布式和随机性，因此一种新的ENF证明（PoENF）算法可以保证以最少的计算资源对音频流进行拜占庭式的抗深度伪造检测。通过利用音频会议或音频编辑应用程序作为前端软件服务，DeFake解决方案可以有效地验证录制视频片段的真实性。
Modern video conferencing technologies provide state-of-the-art end-to-end encryption models but do not verify the authenticity of the media broadcast, where the verification of the media is left to the end-users. A perpetrator can forge the video or audio streams using replay attacks or deepfake attacks to manipulate the real-time perception of transcribed events. Leveraging Electrical Network Frequency (ENF) signals as an environmental fingerprint, this paper proposes a distributed consensus network-based audio authentication scheme named DeFake - Decentralized ENF-consensus based deepFake detection, which detects multimedia manipulations in real-time. Since the fluctuations in an ENF signal are of a distributed and random nature, a novel Proof-of-ENF (PoENF) algorithm can guarantee byzantine resistant deepfakes detection on audio streams with minimal computational resources. By utilizing audio conferencing or audio editing applications as the frontend software service, the DeFake solution can effectively and efficiently verify the authenticity of the recorded video clip.