---
rs9000--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_slow.gif](https://cdn-uploads.huggingface.co/production/uploads/6380ccd084022715e0d49d4e/k_Zs325tahEteMx_Df1fW.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.

```python
from datasets import load_dataset
elsa_data = load_dataset("rs9000/ELSA1M_track1")
```

## 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 (rosario.dicarlo.ext@leonardo.com)
- UNIMORE