Datasets:
Tasks:
Image-to-Image
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
100K - 1M
ArXiv:
License:
| license: openrail | |
| task_categories: | |
| - image-to-image | |
| language: | |
| - en | |
| tags: | |
| - deepfake | |
| - diffusion model | |
| pretty_name: DeepFakeFace' | |
| ``` | |
| --- | |
| license: apache-2.0 | |
| --- | |
| ``` | |
| The dataset accompanying the paper | |
| "Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models". | |
| [[Website](https://sites.google.com/view/deepfakeface/home)] [[paper](https://arxiv.org/abs/2309.02218)] [[GitHub](https://github.com/OpenRL-Lab/DeepFakeFace)]. | |
| ### Introduction | |
| Welcome to the **DeepFakeFace (DFF)** dataset! Here we present a meticulously curated collection of artificial celebrity faces, crafted using cutting-edge diffusion models. | |
| Our aim is to tackle the rising challenge posed by deepfakes in today's digital landscape. | |
| Here are some example images in our dataset: | |
|  | |
| Our proposed DeepFakeFace(DFF) dataset is generated by various diffusion models, aiming to protect the privacy of celebrities. | |
| There are four zip files in our dataset and each file contains 30,000 images. | |
| We maintain the same directory structure as the IMDB-WIKI dataset where real images are selected. | |
| - inpainting.zip is generated by the Stable Diffusion Inpainting model. | |
| - insight.zip is generated by the InsightFace toolbox. | |
| - text2img.zip is generated by Stable Diffusion V1.5 | |
| - wiki.zip contains original real images selected from the IMDB-WIKI dataset. | |
| ### DeepFake Dataset Compare | |
| We compare our dataset with previous datasets here: | |
|  | |
| ### Experimental Results | |
| Performance of RECCE across different generators, measured in terms of Acc (%), AUC (%), and EER (%): | |
|  | |
| Robustness evaluation in terms of ACC(%), AUC (%) and EER(%): | |
|  | |
| ### Cite | |
| Please cite our paper if you use our codes or our dataset in your own work: | |
| ``` | |
| @misc{song2023robustness, | |
| title={Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models}, | |
| author={Haixu Song and Shiyu Huang and Yinpeng Dong and Wei-Wei Tu}, | |
| year={2023}, | |
| eprint={2309.02218}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |