elsaEU/ELSA500k_track2

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/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 daam import WordHeatMap
from datasets import load_dataset
import torch

elsa_data = load_dataset("elsaEU/ELSA500k_track2", split="train", streaming=True)
for sample in elsa_data:
  image = sample.pop("image")
  heatmaps = sample.pop("heatmaps")
  heatmap_labels = sample.pop("heatmap_labels")
  metadata = sample
  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.