Getting Started

The code has been developed for this challenge and is available on Github in two version: Pytorch repo and Tensorflow repo.

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:

  • venv , part of python's standard library, but not included in some python installations

(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.

For Pytorch: 

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 page for downloading the datasets.

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.

For PyTorch, one should go https://pytorch.org/ to install the pytorch version: 2.7.0

For TensorFlow, make sure that the tensorflow version: 2.19.0

*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

The function evaluate() will output something like:

If there are any issues, please reach out to the organizer.

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