contributor_README.md

# HyperForensics++ Dataset Contribution Guide

This guide outlines the steps to contribute to the HyperForensics++ dataset repository.

---

## 1. Clone the Repository
First, clone the repository from Hugging Face to your local machine:
```bash
# Make sure git-lfs is installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/datasets/OtoroLin/HyperForensics-plus-plus
cd ./HyperForensics-plus-plus
```
## 2. Extract the Dataset
Decompress the `.tar.gz` files into a separate local hierarchy. You can utilize the provided `unzipping.sh` script. First, modify the `ROOT_DIR` variable to the `local_data` path
```bash
# In zipping.sh
# Defined the root directory of the whole dataset
ROOT_DIR="/root/to/local_data"
```
Due to the redundancy of our dataset, we use **`pigz`** program instead of standar `gipz` program to compress the files. Make sure you install it first.
```bash
sudo apt install pigz
# If you are using miniconda
conda install pigz
```
### 2.1 Decompressing the specify method and configuration:
```bash
./unzipping.sh --method <method name> --config <config name>
```
Replace `<method name>` with the forgery method and `<config name>` with the configuration index. This will automatically decompressed `.tar.gz` file and put it under hierarchy of the local dataset directory.
For instance, if I want to decompress the images under `ADMM-ADAM` forgery method, `config0` configuration:
```bash
./unzipping.sh --method ADMM-ADAM --config 0
```
This will decompress `HyperForensics-plus-plus/data/ADMM-ADAM/config0/config0.tar.gz` to `/path/to/local/ADMM-ADAM/config0/*`.
### 2.2 Decompress the entire hierarchy
```bash
./unzipping.sh --all
```
This will recursively decompress all `tar.gz` file in the `HyperForensics-plus-plus/data` and automatically put the decompressed files under hierarchy of the local dataset directory.

## 3. [Create a pull request](https://huggingface.co/docs/hub/repositories-pull-requests-discussions#pull-requests-advanced-usage)
The pull requests on Hugging Face do not use forks and branches, but instead custom “branches” called `refs` that are stored directly on the source repo.

The advantage of using custom `refs` (like `refs/pr/42` for instance) instead of branches is that they’re not fetched (by default) by people cloning the repo, but they can still be fetched on demand.
### 3.1 Create a new PR on the hugging face
Go to *community* $\to$ *New pull request* $\to$ *On your machine* $\to$ enter *Pull request title* $\to$ *Creat PR branch*

Then, you shall see a brand new PR page with the title of your PR on the top left. Make note of the index number next to the title `#{index}`.
> In this step, you actually create a new custom branch on **remote** under reference of `ref/pr/#{index}`.

The PR is still in **draft mode**, the maintainer can not merge the PR untill you publish the PR.
### 3.2 Create a new PR branch on local
```bash
git fetch origin refs/pr/<index>
git checkout pr/<index>
```
Fetch the remote PR branch `ref/pr/<index>` to local PR branch. Then checkout to the newly created custom branch under references `pr/`, and just for unambiguity, using the PR index as the name of that reference. You can actually create a new branch locally here as usual.
### 3.3 Push the PR branch
After you finish your modification, push the local PR branch to remote Hugging Face. Check it out [here at 6](#6-create-a-pull-request).

## 4. Modify or Generate Data
Make changes or generate new data in the extracted directory. Ensure the data follows the required structure:
```
local_data/
    |-{attacking method}/
    |   |-config0/
    |   |   |-images.npy
    |   |-config1/
    |   | ...
    |   |-config4/
    |-{attacking method}/
    |   |-config{0-4}/
    |-Origin
    |   |-images.npy
HyperForensics-plus-plus/
    |-data/
```
## 5. Zip the Directory
After modifying or generating data, zip the directory into `.tar.gz` files and place it in the repository.
You can utilize the provided `zipping.sh` script. First, modify the `ROOT_DIR` variable to the `local_data` path
```bash
# In zipping.sh
# Defined the root directory of the whole dataset
ROOT_DIR="/root/to/local_data"
```
Due to the redundancy of our dataset, we use **`pigz`** program instead of standar `gipz` program to compress the files. Make sure you install it first.
```bash
sudo apt install pigz
# If you are using miniconda
conda install pigz
```
### 5.1 Compressing the specify method and configuration:
```bash
./zipping.sh --method <method name> --config <config name>
```
Replace <method name> with the forgery method and <config name> with the configuration index. This will automatically put the compressed `.tar.gz` file under hierarchy of the dataset repo.
For instance, if I want to zip the images under `ADMM-ADAM` forgery method, `config0` configuration:
```bash
./zipping.sh --method ADMM-ADAM --config 0
```
This will compress `/path/to/local/ADMM-ADAM/config0/*` to `HyperForensics-plus-plus/data/ADMM-ADAM/config0/config0.tar.gz`.
### 5.2 Compressing the specify direcotry
```bash
./zipping.sh --dir-path <path/to/directory>
```
Replace `<path/to/directory>` with the intended directory path. This will compress the directory and put it in the working directory.
For instance,
```bash
./zipping.sh --dir-path /path/to/local/ADMM-ADAM/config0
```
This will compress `/path/to/local/ADMM-ADAM/config0/*` to `HyperForensics-plus-plus/config0.tar.gz`.
### 5.3 Compress the entire hierarchy
```bash
./zipping.sh --all
```
This will compress the entire local data hierarchy seperately and automatically put the compressed files under hierarchy of the dataset repo.
> **Note** that I did not imiplement the method/config-specified compressing for **Origin** since it seems unlikely people needs to compress/decompress it regularly. You can either do it manually or use the path-specified compressing method and put it in to hierarchy by hand.

## 6. Create a Pull Request
Push your changes to created PR:
```bash
git push origin pr/<index>:refs/pr/<index>
```
Replace `<index>` with the pull request index. And click the ***Publish*** button on the buttom of the page.
> You can aso leave comment for your PR to showcase where you have modified.
## 6. Wait for merging the Pull Request
Once the pull request is published, the repository maintainer will review and merge it.

---
Thank you for contributing to the HyperForensics++ dataset!

## Divide my study into three main scope
1. The data generation
2. The quality matricies to determind the quality of our dataset
3. The forgery detection baseline models
### The data generation
#### Removal Method
This scope has been almost done by min-zuo with two algorithm 1. ADMM-ADAM; 2. FasyHyIn.
> However, since both strategy looks into spectral domain to inpaint the area. I have to look carefully to make sure it actually **remove** the data instead of recovering it.
> How to do that? I need a **classifier or segmentation model** to detect the image making sure it has a wrong output
#### Object replacement
This scope is under min-zuo's hand. He use CRS_diff diffusion model + harmonization to generate the replacemnt example.
:::warning
The images example above water are hard the current bottleneck.
Needs more attention
:::
#### Adv attack
This is implemented by 恩召, I did not actually ask much about it. Choose one adv-attack strategy. Focusing on highly [transibale](https://zhuanlan.zhihu.com/p/686734868) attack

### Qaulity matrics
#### Spectral Consistency Metrics
These metrics are used in HSI-tampering, which are used to ensure that the forged region's spectrum matches the surrounding context
1. SAM
2. Spectral RMSE
3. Pearson correlation (need more info)
4. EGRAS (need more info)
#### Forgery Detectability Metrics
I think this is the most important one since we are making forgery dataset. We want to show:
- rate of forgery images being mis-classified is high
    1. FNR
    2. Attack Success Rate
    3. Detection Score Drop (detector confidence before/after forgery)
    We can use either a classification model or segmentation model
- rate of forgeries not detected by a detection model is low
#### Visual Inspection
1. User studies - using false color?
2. Visual result figures (origin vs. forged vs. mask)
#### Visual Quality Matrics
This is relatively not important since this is a forgery dataset, how similar the forgery image is to the ground truth can not evaluate the goodness of the forgery
1. PSNR
2. SSIM
3. LPIPS(Learned Perceptual Image Patch Similarity)
### Forgery Detection Baseline Model
From previous paragraph [Forgery Detectability Metrics](#forgery-detectability-metrics). We need:
1. Pixel wise existing forgery detection model
2. Our own modern SOTA detection model for each attacking method