metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: height
      dtype: uint32
    - name: width
      dtype: uint32
    - name: label
      dtype:
        class_label:
          names:
            '0': real
            '1': fake
    - name: generator
      dtype: large_string
    - name: file_id
      dtype: large_string
    - name: description
      dtype: large_string
    - name: positive_prompt
      dtype: large_string
    - name: negative_prompt
      dtype: large_string
    - name: conditioning
      dtype: large_string
    - name: origin_dataset
      dtype: large_string
    - name: paired_real_images
      large_list: large_string
  splits:
    - name: train
      num_bytes: 28171529545
      num_examples: 144000
    - name: validation
      num_bytes: 7019695896
      num_examples: 36000
  download_size: 35178878540
  dataset_size: 35191225441
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
task_categories:
  - image-classification
pretty_name: AI-GenBench (fake part)

AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection

Important: this is the fake part of the AI-GenBench dataset. To re-create the original benchmark, which includes real images, please check the official repository. Important 2: before using, please check the licensing terms of the images included!

Details

The rapid advancement of generative AI has revolutionized image creation, enabling high-quality synthesis from text prompts while raising critical challenges for media authenticity. We present Ai-GenBench, a novel benchmark designed to address the urgent need for robust detection of AI-generated images in real-world scenarios. Unlike existing solutions that evaluate models on static datasets, AI-GenBench introduces a temporal evaluation framework where detection methods are incrementally trained on synthetic images, historically ordered by their generative models, to test their ability to generalize to new generative models, such as the transition from GANs to diffusion models.

More information can be found in our paper: AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection.

Content

This repository, as the name suggests, contains only the fake images of the AI-GenBench dataset. This is the part that gets automatically downloaded when using the simple dataset creation script

License

The images contained in this dataset are obtained from multiple sources.

Please check the repository at https://github.com/MI-BioLab/AI-GenBench for more information on the sources of the data, the content breakdown, and exact filelists. Before using, please make sure you understand where the files are coming from and the related licensing terms.