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---
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
license: cc-by-4.0
---
# 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 |
```python
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 <a href="https://huggingface.co/docs/datasets/stream">streaming=True</a> 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. |