Two-branch Recurrent Network for Isolating Deepfakes in Videos
For method details, please refer to the paper, the video presentation, demo of the system
Deepfake detection in videos. The figure shows our system predic- tions when trained only on FaceForensics++ for a fake video “in the wild” (left) and its pristine video (right). The heatmap bar below the video indicates the likelihood of the video being fake (red) or unmanipulated (green).
Videos and Results
If you find our paper useuful, please cite it as:
@inproceedings{masi2020two,
title={ Two-branch Recurrent Network for Isolating Deepfakes in Videos},
author={Masi, Iacopo and Killekar, Aditya and Mascarenhas, Royston Marian and Gurudatt, Shenoy Pratik and AbdAlmageed, Wael},
booktitle={European Conference on Computer Vision (ECCV)},
year={2020}
}
Contact
For any paper related questions, please contact masi@isi.edu or open a Github issues above. Please, note that we do not provide software support besides simply replying to a few questions. Thank you for your understanding.
License
The Software is made available for academic or non-commercial purposes only. The license is for a copy of the program for an unlimited term. Individuals requesting a license for commercial use must pay for a commercial license.
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