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ELSA1M_track1

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metadata
elsaEU--ELSA1M_track1:
  description: ''
  citation: ''
  homepage: ''
  license: ''
  features:
    image:
      decode: true
      id: null
      dtype: Image
    id:
      dtype: string
      id: null
      _type: Value
    original_prompt:
      dtype: string
      id: null
      _type: Value
    positive_prompt:
      dtype: string
      id: null
      _type: Value
    negative_prompt:
      dtype: string
      id: null
      _type: Value
    model:
      dtype: string
      id: null
      _type: Value
    nsfw:
      dtype: string
      id: null
      _type: Value
    url_real_image:
      dtype: string
      id: null
      _type: Value
    filepath:
      dtype: string
      id: null
      _type: Value
    aspect_ratio:
      feature:
        dtype: int64
        id: null
        _type: Value
      length: -1
      id: null
      _type: Sequence
  post_processed: null
  supervised_keys: null
  task_templates: null
  builder_name: imagefolder
  config_name: default
  version:
    version_str: 0.0.0
    description: null
    major: 0
    minor: 0
    patch: 0
  splits:
    train:
      name: train
      num_bytes: 445926712527.43
      num_examples: 992655
      dataset_name: ELSA1M_track1
  download_checksums: null
  download_size: 223034360161
  post_processing_size: null
  dataset_size: 445926712527.43
  size_in_bytes: 668961072688.4299
license: cc-by-4.0

ELSA - Multimedia use case

elsa_slow.gif

ELSA Multimedia is a large collection of Deep Fake images, generated using diffusion models

Dataset Summary

This dataset was developed as part of the EU project ELSA. Specifically for the Multimedia use-case. Official webpage: https://benchmarks.elsa-ai.eu/ This dataset aims to develop effective solutions for detecting and mitigating the spread of deep fake images in multimedia content. Deep fake images, which are highly realistic and deceptive manipulations, pose significant risks to privacy, security, and trust in digital media. This dataset can be used to train robust and accurate models that can identify and flag instances of deep fake images.

ELSA versions

Name Description Link
ELSA1M_track1 Dataset of 1M images generated using diffusion model https://huggingface.co/datasets/elsaEU/ELSA1M_track1
ELSA500k_track2 Dataset of 500k images generated using diffusion model with diffusion attentive attribution maps [1] https://huggingface.co/datasets/elsaEU/ELSA500k_track2 
from datasets import load_dataset
elsa_data = load_dataset("elsaEU/ELSA1M_track1", split="train", streaming=True)
for sample in elsa_data:
  image = sample.pop("image")
  metadata = sample

Using streaming=True lets you work with the dataset without downloading it.

Dataset Structure

Each parquet file contains nearly 1k images and a JSON file with metadata.

The Metadata for generated images are:

  • ID: Laion image ID
  • original_prompt: Laion Prompt
  • positive_prompt: positive prompt used for image generation
  • negative_prompt: negative prompt used for image generation
  • model: model used for the image generation
  • nsfw: nsfw tag from Laion
  • url_real_image: Url of the real image associated to the same prompt
  • filepath: filepath of the fake image
  • aspect_ratio: aspect ratio of the generated image

Dataset Curators