Journal
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW)
Volume -, Issue -, Pages 1721-1729Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICCVW.2019.00213
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Categories
Funding
- NSF [1822976, 1422257]
- Florida Cybersecurity Center
- Royal Bank of Canada
- Air Force Young Investigator Award
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1822976] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1422257] Funding Source: National Science Foundation
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Deepfake is a technique used to manipulate videos using computer code. It involves replacing the face of a person in a video with the face of another person. The automation of video manipulation means that deepfakes are becoming more prevalent and easier to implement. This can be credited to the emergence of apps like FaceApp and FakeApp, which allow users to create their own deepfake videos using their smartphones. It has hence become essential to detect fake videos, to avoid the spread of false information. A recent study shows that the heart rate of fake videos can be used to distinguish original and fake videos. In the study presented, we obtained the heart rate of original videos and trained the state-of-the-art Neural Ordinary Differential Equations (Neural-ODE) model. We then created deep fake videos using commercial software. The average loss obtained for ten original videos is 0.010927, and ten donor videos are 0.010041. The trained Neural-ODE was able to predict the heart rate of our 10 deepfake videos generated using commercial software and 320 deepfake videos of deepfakeTlMl database. To best of our knowledge, this is the first attempt to train a Neural-ODE on original videos to predict the heart rate offake videos.
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