> For the complete documentation index, see [llms.txt](https://pace-tl.gitbook.io/ches-challenge-2025/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pace-tl.gitbook.io/ches-challenge-2025/quickstart.md).

# Getting Started

The code has been developed for this challenge and is available on Github in two version: [Pytorch repo](https://github.com/pace-tl-ntu/ches2025_pytorch) and [Tensorflow repo](https://github.com/pace-tl-ntu/ches2025_tf).

Although, the repositories are using neural network, there are restriction in the use of template attack or stochastic attack. 

## Installing dependencies:

The framework runs with `python >= 3.8`  and `python <= 3.11`. Futhermore, it requires the following dependencies:&#x20;

* `venv` , part of python's standard library, but not included in some python installations&#x20;

(e.g., run `apt install python3-venv` to obtain it)

* `pip` for most of the python installations (on Ubuntu, `apt install python3-pip`)

We highly **recommend** using a UNIX environment (on Windows, use WSL)

## Cloning repo

First, one can clone the challenge repository.&#x20;

For Pytorch:&#x20;

```
git clone https://github.com/pace-tl-ntu/ches2025_pytorch
```

For Tensorflow:

```
git clone https://github.com/pace-tl-ntu/ches2025_tf.git
```

## Downloading the datasets

See [Datasets ](/ches-challenge-2025/quickstart-2.md)page for downloading the datasets.&#x20;

## Train models during the profiling phase

In the following, we use `./../Datasets` as the path to where the dataset is stored, and `ches_2025` as the directory of the repository.&#x20;

For PyTorch, one should go <https://pytorch.org/> to install the pytorch version:  `2.7.0`&#x20;

For TensorFlow,  make sure that the tensorflow version: `2.19.0`&#x20;

```markup
cd ches_2025
python3 -m venv ches_env
source ches_env/bin/activate
pip install pip --upgrade 
pip install -r requirements.txt

#For Pytorch library, go to https://pytorch.org/
#and install pytorch before running the following command line. 

python3 main_{tf/pytorch}.py #Train Neural Network
deactivate
```

*\*For MacOS, please use Pytorch instead of Tensorflow.*

## Analyze the trained model during the  attack phase

For submission, one should edit the code `analyze_{tf/pytorch}.py`, where they have to load their model to run the function `evaluate()` to compute the guessing entropy. There are some code there&#x20;

```
python3 analyze_{tf/pytorch}.py
```

The function `evaluate()` will output something like:

```
GE [118. 132.99 133.72 137.16 130.53 127. 132.64 131.46 130.2 131.53
135.33 135.04 137.32 138.76 133.96 134.03 135.71 134.04 134.74 137.19
138.68 136.61 136.55 134.75 136.74 137.48 134.59 131.45 132.92 134.83
135.36 134.8 134.29 132.21 132.17 132.22 132.37 131.61 131.63 131.09
131.65 132.17 132.68 132.13 131.18 130.95 130.26 129.85 132.93 133.7
136.28 136.87 138. 137.15 136.42 134.62 135.46 134.45 134.58 134.5
135.64 135.27 134.65 135.73 134.99 135.77 136.71 136.5 136.07 135.33
135.38 136.12 135.65 135.43 133.96 133.75 136.19 138.79 137.45 136.79
137.65 136.69 138.11 138.98 138.11 138.13 137.8 138.46 138.3 137.25
136.35 137.59 137.04 136.75 137.76 138.82 139.51 140.16 139.02 139.83]
NTGE inf
```

If there are any issues, please reach out to the [organizer](mailto:pace.tl.ntu@gmail.com).&#x20;