|
--- |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: train |
|
path: |
|
- data/train-* |
|
- data/val-* |
|
- split: validation |
|
path: data/validation-* |
|
dataset_info: |
|
features: |
|
- name: id |
|
dtype: string |
|
- name: original_prompt |
|
dtype: string |
|
- name: positive_prompt |
|
dtype: string |
|
- name: negative_prompt |
|
dtype: string |
|
- name: url |
|
dtype: string |
|
- name: model_gen0 |
|
dtype: string |
|
- name: model_gen1 |
|
dtype: string |
|
- name: model_gen2 |
|
dtype: string |
|
- name: model_gen3 |
|
dtype: string |
|
- name: width_gen0 |
|
dtype: int64 |
|
- name: width_gen1 |
|
dtype: int64 |
|
- name: width_gen2 |
|
dtype: int64 |
|
- name: width_gen3 |
|
dtype: int64 |
|
- name: height_gen0 |
|
dtype: int64 |
|
- name: height_gen1 |
|
dtype: int64 |
|
- name: height_gen2 |
|
dtype: int64 |
|
- name: height_gen3 |
|
dtype: int64 |
|
- name: num_inference_steps_gen0 |
|
dtype: int64 |
|
- name: num_inference_steps_gen1 |
|
dtype: int64 |
|
- name: num_inference_steps_gen2 |
|
dtype: int64 |
|
- name: num_inference_steps_gen3 |
|
dtype: int64 |
|
- name: filepath_gen0 |
|
dtype: string |
|
- name: filepath_gen1 |
|
dtype: string |
|
- name: filepath_gen2 |
|
dtype: string |
|
- name: filepath_gen3 |
|
dtype: string |
|
- name: image_gen0 |
|
dtype: image |
|
- name: image_gen1 |
|
dtype: image |
|
- name: image_gen2 |
|
dtype: image |
|
- name: image_gen3 |
|
dtype: image |
|
splits: |
|
- name: train |
|
num_bytes: 2626848010531.5 |
|
num_examples: 2306629 |
|
- name: validation |
|
num_bytes: 5318900038.0 |
|
num_examples: 4800 |
|
download_size: 2568003790242 |
|
dataset_size: 2632166910569.5 |
|
--- |
|
|
|
# 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 | |
|
| ELSA10M_track1 | Dataset of 10M images generated using four different diffusion models for each caption, multiple image compression formats, multiple aspect ration | https://huggingface.co/datasets/elsaEU/ELSA_D3 | |
|
| 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/ELSA_D3", split="train", streaming=True) |
|
``` |
|
|
|
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 |
|
- url: Url of the real image associated with the same prompt |
|
- width: width generated image |
|
- height: height generated image |
|
- num_inference_steps: diffusion steps of the generator |
|
- filepath: path of the generated image |
|
- model_gen0: Generator 0 name |
|
- model_gen1: Generator 1 name |
|
- model_gen2: Generator 2 name |
|
- model_gen3: Generator 3 name |
|
- image_gen0: image generated with generator 0 |
|
- image_gen1: image generated with generator 1 |
|
- image_gen2: image generated with generator 2 |
|
- image_gen3: image generated with generator 3 |
|
- aspect_ratio: aspect ratio of the generated image |
|
|
|
|
|
### Dataset Curators |
|
|
|
- Leonardo Labs (rosario.dicarlo.ext@leonardo.com) |
|
- UNIMORE (https://aimagelab.ing.unimore.it/imagelab/) |
