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<h2 style="text-align:center;">WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection</h2> <p align="center"> <img src="./fakemask.jpg" width="720" height="435px" alt="Deepfake in the Wild" title="Deepfake in the Wild" align="center"></img> </p>Dataset Description
Existing deepfake datasets like DeepfakeDetection and FaceForensics++ have advanced detection research but are limited by constrained real videos featuring a few actors and fake videos generated using popular software. As a result, detectors trained on these datasets often struggle with the diversity of real-world deepfakes found online.
To address this, we introduce WildDeepfake, a dataset of 7,314 face sequences from 707 deepfake videos sourced entirely from the internet. Despite its small size, WildDeepfake better represents the challenges of real-world detection, where baseline detectors show significantly reduced performance.
To enhance detection, we also propose Attention-based Deepfake Detection Networks (ADDNets), utilizing 2D and 3D attention mechanisms to improve focus on real/fake facial features.
Dataset Statistics
Previous datasets
| Dataset name | Download | Generate method | Deepfake videos | Actors |
|---|---|---|---|---|
| Deepfake-TIMIT low | download | Deepfake | 320 | 32 |
| Deepfake-TIMIT high | download | Deepfake | 320 | 32 |
| Faceforensics | - | Deepfake | 1000 | 977 |
| Faceforensics++ | download | Deepfake | 1000 | 977 |
| Deepfake detection | download | Deepfake | over3000 | 28 |
| Celeb-deepfakeforensics v1 | download | Deepfake | 795 | 13 |
| Celeb-deepfakeforensics v2 | download | Deepfake | 590 | 59 |
| DFDC | download | Deepfake | - | - |
Ours
| Dataset name | Download | Generate method | Deepfake videos | Actors |
|---|---|---|---|---|
| WildDeepfake | download | Internet | 707 | - |
File Structure:
deepfake_in_the_wild
|--real train
|--0.tar.gz
|--1.tar.gz
|--2.tar.gz
...
|--real test
|--0.tar.gz
|--1.tar.gz
|--2.tar.gz
...
|--fake train
|--0.tar.gz
|--1.tar.gz
|--2.tar.gz
...
|--fake test
|--0.tar.gz
|--1.tar.gz
|--2.tar.gz
...
In each tar.gz file, there will be several folders containing face images, and the images in each folder represent a face sequence. The image name in the folder represents the frame number it appears in the original video.
<!--- ### Our ADDNet-2D Detection Method <p align="center"> <img src="./ADDNet.png" alt="ADDNet" title="ADDNet" align="center"></img> </p> The network structure of our proposed ADDNet-2D is illustrated below. Detailed structures of the three residual blocks used in our ADDNet-2D network are shown below too. These three blocks are also the building blocks of XceptionNet. The base network before the "resblock3" is our proposed ADDblock. Our ADDNet-3D shares the same ADD block architecture as ADDNet-2D, but has one ADD block for each of the face images in the sequence. Therefore, in our setting with face sequence length 𝐿, ADDNet-3D will have 𝐿 ADDblocks, and each ADD blocks share the same weights. Also different from ADDNet-2D, the classifier network(structure after the ADDblock) of ADDNet-3D is a 3D CNN. <p align="center"> <img src="./details.jpg" alt="details" title="detials" align="center"></img> </p> ### Expriments First, we use pre-trained [Resnet-101](https://github.com/tensorflow/models/tree/master/research/slim) to extract features from the images in previous datasets and our dataset. Then we use the [T-SNE](http://projector.tensorflow.org/) to reduce the dimensionality. Red points represent fake faces, green points represent real faces. Here is the comparison: <p align="center"> <img src="./t-sne.PNG" alt="t-sne" title="t-sne" align="center"></img> </p> --->Request for Download
You can fill the form here to request a copy of the dataset. We support both Baidu Drive and Google Drive.
Privacy Statement
To ensure the privacy of individuals featured in the dataset, we have implemented the following measures:
- Restricted Use: The dataset is strictly for research purposes, and only face sequences are released, not full videos.
- Privacy Protection in Publications: Key facial features are obscured in all visual materials, including papers and presentations. Additionally, strict access controls are in place.
- Applicant Verification: Access is granted only after verifying the applicant’s academic email address, personal electronic signature, and other necessary credentials.
- Usage Agreement: Applicants are required to sign a comprehensive agreement to ensure the dataset is used exclusively for research purposes.
- Right to Removal: If any part of the dataset impacts you, please contact us to request its removal.
We are committed to safeguarding privacy while enabling research advancements.