metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: id
      dtype: string
    - name: original_prompt
      dtype: string
    - name: positive_prompt
      dtype: string
    - name: negative_prompt
      dtype: string
    - name: model
      dtype: string
    - name: filepath
      dtype: string
    - name: num_inference_steps
      dtype: int64
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: url
      dtype: string
    - name: image
      dtype: image
    - name: heatmap_labels
      sequence: string
    - name: heatmaps
      sequence:
        sequence:
          sequence: float64
  splits:
    - name: train
      num_bytes: 127788930013
      num_examples: 501000
  download_size: 54902331553
  dataset_size: 127788930013

ELSA - Multimedia use case

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/rs9000/ELSA1M_track1
ELSA500k_track2	Dataset of 500k images generated using diffusion model with diffusion attentive attribution maps [1]	https://huggingface.co/datasets/rs9000/ELSA500k_track2

from daam import WordHeatMap
from datasets import load_dataset
import torch

elsa_data = load_dataset("rs9000/ELSA500k_track2", split="train", streaming=True)
for sample in elsa_data:
  image = sample.pop("image")
  metadata = sample
  heatmaps = sample.pop("heatmaps")
  heatmap_labels = sample.pop("heatmap_labels")
  for j, (h, l) in enumerate(zip(heatmaps, heatmap_labels)):
    heatmap = WordHeatMap(torch.Tensor(h), word=l)
    heatmap.plot_overlay(image)
    plt.show()

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
heatmaps: diffusion attentive attribution maps
heatmap_labels: words releated to the heatmaps

Dataset Curators

Leonardo Labs (rosario.dicarlo.ext@leonardo.com)
UNIMORE (https://aimagelab.ing.unimore.it/imagelab/)

References

[1] What the DAAM: Interpreting Stable Diffusion Using Cross Attention, 2023. Tang Raphael et al.