DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 22 Jul 2022.
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9 of 30 references.
References · 9
- 2022Generative Adversarial Networkscited by 5,627
- 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 504
- 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 280
- 2020Media Forensics and DeepFakes: an overviewcited by 209
- 2018Do GANs leave artificial fingerprints?cited by 190
- 2021FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarioscited by 6
- 2020Zooming Into Video Conferencing Privacy and Security Threatscited by 5
- 2018Real-Time Index Authentication for Event-Oriented Surveillance Video Query using Blockchaincited by 2
- 2019Analysis of Rolling Shutter Effect on ENF based Video Forensicscited by 2
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