D-Rep / README.md
WenhaoWang's picture
Update README.md
2d84c4f verified
---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 10K<n<100K
task_categories:
- text-to-image
- image-feature-extraction
tags:
- diffusion models
- image copy detection
dataset_info:
features:
- name: Name
dtype: string
- name: Level
dtype: int64
- name: generated_images
dtype: image
- name: real_images
dtype: image
splits:
- name: Test
num_bytes: 2538590040
num_examples: 4000
- name: Train
num_bytes: 22265208436
num_examples: 36000
download_size: 24773596239
dataset_size: 24803798476
configs:
- config_name: default
data_files:
- split: Test
path: data/Test-*
- split: Train
path: data/Train-*
pretty_name: '='
---
<p align="center">
<img src="https://huggingface.co/datasets/WenhaoWang/D-Rep/resolve/main/D-Rep.png" width="800">
</p>
# Summary
This is the dataset proposed in our paper [**Image Copy Detection for Diffusion Models**](https://arxiv.org/abs/2409.19952) (NeurIPS 2024).
D-Rep consists of 40, 000 image-replica pairs, in which each replica is generated by a diffusion model. The 40, 000 image-replica pairs are manually labeled with 6 replication levels ranging from 0 (no replication) to 5 (total replication). We divide D-Rep into a training set with 90% (36, 000) pairs and a test set with the remaining 10% (4, 000) pairs.
# Download
### Automatical
Install the [datasets](https://huggingface.co/docs/datasets/en/installation) library first, by:
```
pip install datasets
```
Then it can be downloaded automatically with
```python
from datasets import load_dataset
dataset = load_dataset('WenhaoWang/D-Rep')
```
### Manual
You can also download each file by ```wget```:
```
wget https://huggingface.co/datasets/WenhaoWang/D-Rep/resolve/main/training_pairs.tar
wget https://huggingface.co/datasets/WenhaoWang/D-Rep/resolve/main/test_pairs.tar
wget https://huggingface.co/datasets/WenhaoWang/D-Rep/resolve/main/labels.csv
```
# Curators
D-Rep is created by [Wenhao Wang](https://wangwenhao0716.github.io/), Dr. [Yifan Sun](https://yifansun-reid.github.io/), [Zhentao Tan](https://scholar.google.com.hk/citations?user=jDsfBUwAAAAJ) and Professor [Yi Yang](https://scholar.google.com/citations?user=RMSuNFwAAAAJ).
# License
We release our D-Rep under the [CC-BY-NC-4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en).
# Helpful Links
The project homepage: https://icdiff.github.io/
The code of image copy detection for diffusion models: https://github.com/WangWenhao0716/PDF-Embedding
The official reviews of our paper: https://openreview.net/forum?id=gvlOQC6oP1
The Arxiv: https://arxiv.org/abs/2409.19952
# Citation
```
@article{wang2024icdiff,
title={Image Copy Detection for Diffusion Models},
author={Wang, Wenhao and Sun, Yifan and Tan, Zhentao and Yang, Yi},
booktitle={Thirty-eighth Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gvlOQC6oP1}
}
```
# Contact
If you have any questions, feel free to contact Wenhao Wang (wangwenhao0716@gmail.com).