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https://github.com/aimagelab/unveiling-the-truth

Last synced: 30 days ago
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Host: GitHub
URL: https://github.com/aimagelab/unveiling-the-truth
Owner: aimagelab
Created: 2024-01-17T13:26:08.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-04-30T08:53:04.000Z (9 months ago)
Last Synced: 2024-12-17T01:03:22.103Z (about 1 month ago)
Language: Python
Size: 2.1 MB
Stars: 2
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

Awesome-Segment-Anything - [code

README

        # Unveiling the Truth (IEEE Signal Processing Letters 2024)

### Exploring Human Gaze Patterns in Fake Images 

[**Giuseppe Cartella**](https://scholar.google.com/citations?hl=en&user=0sJ4VCcAAAAJ),

[**Vittorio Cuculo**](https://scholar.google.it/citations?user=usEfqxoAAAAJ&hl=it),

[**Marcella Cornia**](https://scholar.google.com/citations?hl=en&user=DzgmSJEAAAAJ),

[**Rita Cucchiara**](https://scholar.google.com/citations?hl=en&user=OM3sZEoAAAAJ)

This is the **official repository** for the paper "*Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images*".

## 📣 Latest News 📣

- **`30 April 2024`** Dataset has been released!

## Overview



    



## Download the dataset

Dataset download is available [here](https://github.com/aimagelab/unveiling-the-truth/releases/download/v0.1.0/dataset.zip).

The dataset should be structured as follows:

```

|-dataset/

    |-images/

        |-original/

            |- .jpg

            |- ...

        |-pix2pix_magicbrush/

            |- .jpg

            |- ...

        |-semantic_agnostic/

            |- .jpg

            |- ...

        |-semantic_aware/

            |- .jpg

            |- ...

    |-masks/

        |-semantic_agnostic/

            |- .png

            |- ..

        |-semantic_aware/

            |- .png

            |- ...

    |-gaze_data.pickle

    |-ip2p_edit_instruction_ADE_subset.json

    |-ip2p_edit_instruction_COCO_subset.json

    |-ip2p_edit_instruction_LHQ_subset.json

    |-semantic_aware_prompt_ADE_subset.json

    |-semantic_aware_prompt_COCO_subset.json

    |-semantic_aware_prompt_LHQ_subset.json

    |-readme.txt

```

Please refer to the **readme.txt** file for all details.

## How to Visualize Scanpaths

As a first step, move the dataset into the project directory.

To visualize a scanpath run the following command:

```sh

python visualize_scanpath.py --img_id COCO_000000229478 --user=4 --type=semantic_aware

```

By default, two output files are generated:

- output_raw.png --> It contains raw fixations (both saccades and fixations)

- output_fixations.png --> It contains only the final processed fixations.

Red and blue circles represent the first and latest fixation of the plotted scanpath, respectively.

## TODO

- [x] Dataset release