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---
elsaEU--ELSA1M_track1:
description: ''
citation: ''
homepage: ''
license: ''
features:
image:
decode: true
id:
dtype: Image
id:
dtype: string
id:
_type: Value
original_prompt:
dtype: string
id:
_type: Value
positive_prompt:
dtype: string
id:
_type: Value
negative_prompt:
dtype: string
id:
_type: Value
model:
dtype: string
id:
_type: Value
nsfw:
dtype: string
id:
_type: Value
url_real_image:
dtype: string
id:
_type: Value
filepath:
dtype: string
id:
_type: Value
aspect_ratio:
feature:
dtype: int64
id:
_type: Value
length: -1
id:
_type: Sequence
post_processed:
supervised_keys:
task_templates:
builder_name: imagefolder
config_name: default
version:
version_str: 0.0.0
description:
major: 0
minor: 0
patch: 0
splits:
train:
name: train
num_bytes: 445926712527.43
num_examples: 992655
dataset_name: ELSA1M_track1
download_checksums:
download_size: 223034360161
post_processing_size:
dataset_size: 445926712527.43
size_in_bytes: 668961072688.4299
---
# 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 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 <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
### Dataset Curators
- Leonardo Labs (rosario.dicarlo.ext@leonardo.com)
- UNIMORE (https://aimagelab.ing.unimore.it/imagelab/)
|