nikhilny25/ff-images-dataset

---
license: cc-by-nc-4.0
task_categories:
- image-classification
tags:
- deepfake-detection
- faceforensics
- computer-vision
- binary-classification
size_categories:
- 100K<n<1M
---

# FaceForensics++ Image Dataset

This dataset contains preprocessed images from the FaceForensics++ benchmark for deepfake detection.

## Dataset Description

- **Total Images:** 223,919
- **Real Images:** 32,000
- **Fake Images:** 191,919
- **Imbalance Ratio:** 6.00:1 (fake:real)

### Categories

| Category | Count |
|----------|-------|
| original | 32,000 |
| Deepfakes | 32,000 |
| Face2Face | 32,000 |
| FaceSwap | 32,000 |
| NeuralTextures | 32,000 |
| FaceShifter | 32,000 |
| DeepFakeDetection | 31,919 |

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("RohanRamesh/ff-images-dataset")

# Access splits
train_data = dataset['train']
val_data = dataset['validation']
test_data = dataset['test']

# Example: iterate over training data
for sample in train_data:
image = sample['image'] # PIL Image
label = sample['label'] # 0 = FAKE, 1 = REAL
category = sample['category'] # e.g., 'original', 'Deepfakes', etc.
```

## Dataset Structure

Each sample contains:
- `image`: The face image (PIL Image)
- `label`: Binary label (0 = FAKE, 1 = REAL)
- `category`: Original category (original, Deepfakes, Face2Face, FaceSwap, FaceShifter, NeuralTextures, DeepFakeDetection)
- `video_id`: Source video identifier
- `frame_number`: Frame number within the video
- `label_text`: Text label ("REAL" or "FAKE")

## Splits

The dataset is split by video ID to prevent data leakage:
- **Train:** 80% of videos
- **Validation:** 10% of videos
- **Test:** 10% of videos

## Citation

If you use this dataset, please cite the original FaceForensics++ paper:

```bibtex
@inproceedings{roessler2019faceforensicspp,
author = {Rossler, Andreas and Cozzolino, Davide and Verdoliva, Luisa and Riess, Christian and Thies, Justus and Niessner, Matthias},
title = {FaceForensics++: Learning to Detect Manipulated Facial Images},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2019}
}
```