ML-PePR (Beta)

ML-PePR stands for Machine Learning Pentesting for Privacy and Robustness and is a Python library for evaluating machine learning models. PePR is easily extensible and hackable. PePR’s attack runner allows structured pentesting, and the report generator produces straightforward privacy and robustness reports (LaTeX/PDF) from the attack results.

Warning

Caution, we cannot guarantee the correctness of PePR. Always do check the plausibility of your results!

Installation

We offer various installation options. Follow the instructions below to perform the desired installation. If you want to install the latest developer version, please use the code-repository of this library. The current release is only tested with Python 3.6.

Repository

  1. Clone the repository.
  2. Cd to project directory: cd mlpepr
  3. Run in the terminal: pip install .

PyPi

pypi-typical: pip install mlpepr

Docker

To use PePR inside a docker container, build a CPU or GPU image. Note that your system must be set up for GPU use: TensorFlow Docker Requirements.

Build the docker image:

  1. Clone the repository: git clone https://github.com/hallojs/ml-pepr.git
  2. Cd to project directory: cd ml-pepr
  3. Build the docker image: docker build -t <image name> . -f Dockerfile-tf-<cpu or gpu>

Note

The current version is not tested under Windows and only supports TensorFlow at the Moment!