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🌐 ARTIFICIAL INTELLIGENCE 2.0 ™ : FOR MAKING A DEEP FAKE DETECTION PROXIA B
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[*] Software Requirements: Google Colab/Jupyter Notebook, Python, Tensor Flow
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Prerequisite reading:
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PROMOTIONAL MATERIAL FOR 𝗠𝗔𝗚𝗡𝗘𝗧𝗥𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬 ™. (CUSTOM GRAPHICS BY 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗗𝗘𝗦𝗬𝗚𝗡 ™/𝗢𝗦𝗔𝗥𝗢 𝗛𝗔𝗥𝗥𝗜𝗢𝗧𝗧). THE 𝗠𝗔𝗚𝗡𝗘𝗧𝗥𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬 ™ SYMBOL/LOGO IS A TRADEMARK OF 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™ FOR 𝗠𝗔𝗚𝗡𝗘𝗧𝗥𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬 ™. 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™ SYMBOL/LOGO IS A TRADEMARK OF 𝗧𝗛𝗘 𝗔𝗕𝗖 𝟭𝟮𝟯 𝗚𝗥𝗢𝗨𝗣 ™. *️⃣📶🤖
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FaceForensics++ is a forensics dataset consisting of 1000 original video sequences that have been manipulated with four automated face manipulation methods: Deepfakes, Face2Face, FaceSwap and NeuralTextures. The data has been sourced from 977 youtube videos and all videos contain a trackable mostly frontal face without occlusions which enables automated tampering methods to generate realistic forgeries. As we provide binary masks the data can be used for image and video classification as well as segmentation. In addition, we provide 1000 Deepfakes models to generate and augment new data.
For more information, please consult our updated paper.
After a power outage, our EU servers are up again. Unfortunately, we are still have some issues with the Canadian server (CA). Please use the EU hosts (EU, EU2) for now until we resolve the issue and remove this message.
- FaceShifter: We are including the two-stage FaceShifter face swapping method that has been published in CVPR2020. It is able to generate high fidelity identity preserving face swap results and, in comparison to our previous methods, deal with facial occlusions using a second synthesis stage consisting of a Heuristic Error Acknowledging Refinement Network (HEAR-Net). All 1000 original videos of the original youtube based dataset have been manipulated. Please check them out on their project page for more information! See its dataset page for updated numbers as well as an example video. If you want to access the new data and have already applied for our download script, simply reuse the original download link to get the updated script. Otherwise, please fill out this google form and, once accepted, we will send you the link to our download script.
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Deep Fake Detection Dataset: We are hosting the Deep Fake Detection Dataset provided by Google & JigSaw. The dataset contains over 3000 manipulated videos from 28 actors in various scenes. The dataset has a similar file structure and is downloaded by default together with the regular dataset. See the dataset page for more information.
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Neural Textures: We included a fourth manipulation method that does face manipulation using GANs and Neural Textures. All results have been updated to incorporate the new manipulation method and we have updated the benchmark as well. We refer to the paper for more information. Unfortunately, we won't continue support on the old benchmark after this update, though you can still submit your models to the new benchmark by creating a new submission.
If you would like to download the FaceForensics++ dataset, please fill out this google form and, once accepted, we will send you the link to our download script.
If you have not received a response within a week, it is likely that your email is bouncing - please check this before sending repeat requests.
Once, you obtain the download link, please head to the download section. You can also find details about the generation of the dataset there.
We are offering an automated benchmark for facial manipulation detection on the presence of compression based on our manipulation methods that contains 1000 images. If you are interested to test your approach on unseen data, check it out! For more information, please consult our paper. You can download the benchmark images here.
You can view the original FaceForensics github here. Any request to this dataset will also contain the download link to the original version of our dataset.
If you use the FaceForensics++ data or code please cite:
@inproceedings{roessler2019faceforensicspp,
author = {Andreas R\"ossler and Davide Cozzolino and Luisa Verdoliva and Christian Riess and Justus Thies and Matthias Nie{\ss}ner},
title = {Face{F}orensics++: Learning to Detect Manipulated Facial Images},
booktitle= {International Conference on Computer Vision (ICCV)},
year = {2019}
}
If you have any questions, please contact us at [email protected].
Please view our youtube video here.
15.07.2020: Added FaceShifter
23.09.2019: Added sample videos as well as the Deep Fake Detection Dataset
30.08.2019: Paper got accepted to ICCV 2019! Updated the download script to include NeuralTextures and changed instructions
06.04.2019: Updated sample and added benchmark
02.04.2019: Updated our arxiv paper, switched to google forms, release of dataset generation methods and added a classification sample
25.01.2019: Release of FaceForensics++
The data is released under the FaceForensics Terms of Use, and the code is released under the MIT license.
Copyright (c) 2019