File size: 3,056 Bytes
3b2f6c3
7f1a7ba
 
c58e6f9
7f1a7ba
 
3b2f6c3
 
 
 
 
 
7f1a7ba
 
 
 
 
 
 
 
 
 
 
 
4d011c3
7f1a7ba
d21f4cb
4d011c3
d21f4cb
 
4d011c3
7f1a7ba
 
 
 
 
d21f4cb
 
4d011c3
7f1a7ba
0562b5b
 
 
 
79faba2
 
2d84c4f
79faba2
 
ca3f1b8
 
 
 
 
 
 
 
 
 
 
 
32bf9e0
 
 
 
 
 
 
 
 
 
c374254
 
 
 
 
 
 
27ae517
c374254
95d2a48
 
 
 
 
 
 
 
b535ad1
 
c374254
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
---
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).