AI-Face-FairnessBench

March 2, 2026 · View on GitHub

AI-Face-FairnessBench

This repository is the official implementation of our paper AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark

🎉🎉🎉 Our AI-Face has been accepted by CVPR 2025!

🚨Call for Participation: NeurIPS 2025 Competition on Fairness in AI Face Detection!

We’re thrilled to announce the Competition of Fairness in AI Face Detection, held at the 2nd Workshop on New Trends in AI-Generated Media and Security (AIMS) @ NeurIPS 2025

🔗Competition Website: https://sites.google.com/view/aifacedetection/home

Welcome to our work AI-Face, for fairness benchmark in AI-generated face detection.

In this work, we propose: (1) a million-scale demographically annotated with 37 distinct generations methods; and (2) a comprehensive fairness benchmark for training, evaluation, and analysis.

AI-Face Dataset Highlight: The key features of our proposed AI-Face dataset are as follows

✅ Demographic Annotation: AI-Face provides Skin Tone, Gender, Age annotations, which are highly salient to measuring bias.

✅ Forgery Diversity: AI-Face comprises 37 distinct deepfake techniques from Deepfake Vidoes, GAN, and Diffusion Models (both representive and SOTA methods are included), facilitating the detection of nowadays' SOTA deepfakes and AIGCs.

✅ Forgery Scale: AI-Face offers million-level AI-generated data scale for face image.

License

The AI-Face Dataset is licensed under CC BY-NC-ND 4.0

Download

You can access and download the images of AI-Face dataset here.

You can download the annotations here If you have any questions, please send an email to lin1785@purdue.edu, hu968@purdue.edu

1. Installation

You can run the following script to configure the necessary environment:

cd AI-Face-FairnessBench
conda create -n FairnessBench python=3.9.0
conda activate FairnessBench
pip install -r requirements.txt

2. Dataset Preparation and Description

After getting our AI-Face dataset, put the provided train.csv and test.csv within AI-Face dataset under ./dataset.

train.csv and test.csv is formatted:

Column	Description
Image Path	Path to the image file
Gender	Gender label: 1 - Male, 0 - Female
Age	Age label: 0 - Child, 1 - Youth, 3 - Adult, 4 - Middle-aged, 5-Senior
Skin Tone	Skin Tone label: Monk Skin Tone Scale
Intersection	0-(Female,Light), 1-(Female,Medium), 2-(Female,Dark), 3-(Male,Light), 4-(Male,Medium), 5-(Male,Dark)
Target	Label indicating real (0) or fake (1) image

Instructions

Download image tar files.
Untar each file.
Organize the data as shown below:

AI-Face Dataset

├── deepfakes
  ├── dfd
  ├── dfdc
  ├── ...
├── GANs
  ├── AttGAN
  ├── STGAN
  ├── ...
├── DMs
  ├── Palette
  ├── StableDiffusion1.5
  ├── ...
├── Real
  ├── FFHQ
  ├── imdb_wiki

3. Load Pretrained Weights

Before running the training code, make sure you load the pre-trained weights. You can download Xception model trained on ImageNet (through this link).

4. Train

To run the training code, you should first go to the ./training/ folder, then run train_test.py:

cd training

python train_test.py

You can adjust the parameters in train_test.py to specify the parameters, e.g., model, batchsize, learning rate, etc.

--lr: learning rate, default is 0.0005.

--train_batchsize: batchsize for training, default is 128.

--test_batchsize: batchsize for testing, default is 32.

--datapath: /path/to/dataset.

--model: detector name ['xception', 'efficientnet', 'core', 'ucf', 'srm', 'f3net', 'spsl', 'daw_fdd', 'dag_fdd', 'fair_df_detector'], default is 'xception'.

--dataset_type: dataset type loaded for detectors, default is 'no_pair'. For 'ucf' and 'fair_df_detector', it should be 'pair'.

📝 Note

To train ViT-b/16 and UnivFD, please run train_test_vit.py and train_test_clip.py, respectively.

📦 Provided Detectors

	File name	Paper
Xception	xception_detector.py	Xception: Deep learning with depthwise separable convolutions
EfficientNet-B4	efficientnetb4_detector.py	Efficientnet: Rethinking model scaling for convolutional neural networks
ViT-B/16	train_test_vit.py	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
UCF	ucf_detector.py	UCF: Uncovering Common Features for Generalizable Deepfake Detection
UnivFD	train_test_clip.py	Towards Universal Fake Image Detectors that Generalize Across Generative Models
CORE	core_detector.py	CORE: Consistent Representation Learning for Face Forgery Detection
F3Net	f3net_detector.py	Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
SRM	srm_detector.py	Generalizing Face Forgery Detection with High-frequency Features
SPSL	spsl_detector.py	Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
DAW-FDD	daw_fdd.py	Improving Fairness in Deepfake Detection
DAG-FDD	dag_fdd.py	Improving Fairness in Deepfake Detection
PG-FDD	fair_df_detector.py	Preserving Fairness Generalization in Deepfake Detection

🚀 Checkpoints

Checkpoints of detectors trained on our AI-Face can be downloaded through the link.

📢 Update Notes

This is our second version of the dataset; here, we list the key differences between the first version. For more details on the initial version, refer to our paper.

Annotation Difference. We have Gender, Age, and Race categories in the first version. See the updated annotations in the second version.

Column	Description
Image Path	Path to the image file
Uncertainty Score Gender	Uncertainty score for gender annotation
Uncertainty Score Age	Uncertainty score for age annotation
Uncertainty Score Race	Uncertainty score for race annotation
Ground Truth Gender	Gender label: 1 - Male, 0 - Female
Ground Truth Age	Age label: 0 - Young, 1 - Middle-aged, 2 - Senior, 3 - Others
Ground Truth Race	Race label: 0 - Asian, 1 - White, 2 - Black, 3 - Others
Intersection	0-(Male,Asian), 1-(Male,White), 2-(Male,Black), 3-(Male,Others), 4-(Female,Asian), 5-(Female,White), 6-(Female,Black), 7-(Female,Others)
Target	Label indicating real (0) or fake (1) image

We used VGGFace2 for annotator training in the first version, while IMDB-WIKI, the gender and age labels from IMDB-WIKI were crawled from Wikipedia and IMDb website, which makes sure the label quality is higher and more reliable.
The difference in the subsets of different versions of AI-Face includes:

Category	AI-Face v1	AI-Face v2
Deepfake Video Datasets	FF++, DFDC, DFC, Celeb-DF-v2	FF++, DFDC, DFD, Celeb-DF-v2
GAN Models (10 total)	AttGAN, MMDGAN, StarGAN, StyleGANs, MSGGAN, ProGAN, STGAN, VQGAN	AttGAN, MMDGAN, StarGAN, StyleGANs, MSGGAN, ProGAN, STGAN, VQGAN
DM Models (8 total)	DALLE2, IF, Midjourney, DCFace, Latent Diffusion, Palette, Stable Diffusion v1.5, Stable Diffusion Inpainting	DALLE2, IF, Midjourney, DCFace, Latent Diffusion, Palette, Stable Diffusion v1.5, Stable Diffusion Inpainting
Fake Face Images	1,245,660	1,245,660
Real Source Datasets	FFHQ, CASIA-WebFace, IMDB-WIKI, CelebA, real images from FF++, DFDC, DFD, Celeb-DF-v2	FFHQ, IMDB-WIKI, real images from FF++, DFDC, DFD, Celeb-DF-v2
Total Real Face Images	866,096	400,885
Total Subsets	30	28
Generation Methods	5 in FF++, 5 in DFD, 8 in DFDC, 1 in A-Celeb-DF-v2, 10 GANs, 8 DMs	5 in FF++, 5 in DFD, 8 in DFDC, 1 in Celeb-DF-v2, 10 GANs, 8 DMs

If you use the AI-face dataset in your research, please cite our paper as:

@inproceedings{lin2025aiface,
  title={AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark},
  author={Li Lin and Santosh and Mingyang Wu and Xin Wang and Shu Hu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2025}
}

Acknowledgement

We acknowledge that part of our code is adapted from DeepfakeBench (NeruIPS 2023), if you cite our paper, please consider citing their paper as well:

@article{yan2023deepfakebench,
  title={Deepfakebench: A comprehensive benchmark of deepfake detection},
  author={Yan, Zhiyuan and Zhang, Yong and Yuan, Xinhang and Lyu, Siwei and Wu, Baoyuan},
  journal={arXiv preprint arXiv:2307.01426},
  year={2023}
}