Duplicate from OpenRL/DeepFakeFace

Co-authored-by: Shiyu Huang <ShiyuHuang@users.noreply.huggingface.co>

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README.md

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+---
+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:
+![deepfake_examples](docs/images/deepfake_examples.jpg)
+
+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:
+![compare](docs/images/compare.jpg)
+
+### Experimental Results
+
+Performance of RECCE across different generators, measured in terms of Acc (%), AUC  (%), and EER (%):
+![table1](docs/images/table1.jpg)
+
+Robustness evaluation in terms of ACC(%), AUC (%) and EER(%):
+![table1](docs/images/table2.jpg)
+
+### 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}
+}
+```

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