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deepfake dataset collected on the web for deepfake detection

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ADDNet.png
details.jpg
DF-TIMIT.png
DFD.png
DFDC.png
fakemask.jpg
FF++.png
README.md
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WD.png

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<h2>WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection</h2>

## 📌 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 Contents
   1. **A comparision to previous datasets (before our work)**
   
   |       Dataset name       |         Download         |Generate method|      Deepfake videos     |           Actors           |
   |--------------------------|--------------------------|----|--------------------------|----------------------------|
   |   Deepfake-TIMIT low     |[download](https://www.idiap.ch/dataset/deepfaketimit)|Deepfake|320|32|
   |   Deepfake-TIMIT high    |[download](https://www.idiap.ch/dataset/deepfaketimit)|Deepfake|320|32|
   |   Faceforensics          |-|Deepfake|1000|977|
   |   Faceforensics++        |[download](https://github.com/ondyari/FaceForensics)|Deepfake|1000|977|
   |   Deepfake detection     |[download](https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html)|Deepfake|over3000|28|
   |Celeb-deepfakeforensics v1|[download](https://github.com/danmohaha/celeb-deepfakeforensics)|Deepfake|795|13|
   |Celeb-deepfakeforensics v2|[download](https://github.com/danmohaha/celeb-deepfakeforensics)|Deepfake|590|59|
   |   DFDC                   |[download](https://deepfakedetectionchallenge.ai/)|Deepfake|-|-|
   |   **WildDeepfake**   |[**download**](https://huggingface.co/datasets/xingjunm/WildDeepfake)|**Internet**|**707**|**Unknown**|

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:

## ⬇️ Request for Download
You will need to complete an agreement [form](https://forms.gle/o8vy9Q8fQ5mQZ4Qk6) to use the dataset. **⚠️ However, please note that due to the high volume of requests, we may not reply to your email unless your request is rejected.**

The dataset is now avalibble on Hugging Face [click to download](https://huggingface.co/datasets/xingjunm/WildDeepfake).

## 📜 Cite Us
If you use this dataset in your research, please cite it as follows:

```
@inproceedings{zi2020wilddeepfake,
  title={Wilddeepfake: A challenging real-world dataset for deepfake detection},
  author={Zi, Bojia and Chang, Minghao and Chen, Jingjing and Ma, Xingjun and Jiang, Yu-Gang},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={2382--2390},
  year={2020}
}
```

## 📝 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](mailto:danxjma@gmail.com) to request its removal.

We are committed to safeguarding privacy while enabling research advancements.

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