metadata

language:
  - en
license: mit
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-*

Summary

This is the dataset proposed in our paper Image Copy Detection for Diffusion Models (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 library first, by:

pip install datasets

Then it can be downloaded automatically with

import numpy as np
from datasets import load_dataset
dataset = load_dataset('WenhaoWang/D-Rep')