Metadata-Version: 2.1
Name: mlpepr
Version: 0.1b4
Summary: PePR is a library for pentesting the privacy risk and robustness of machine learning models.
Home-page: https://github.com/hallojs/ml-pepr
Author: University of Luebeck: ITS KI-Lab Group
Author-email: jonas.sander@t-online.de
License: GPL
Project-URL: Bug Tracker, https://github.com/hallojs/ml-pepr/issues
Project-URL: Documentation, https://hallojs.github.io/ml-pepr/
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: adversarial-robustness-toolbox (==1.6.2)
Requires-Dist: matplotlib (==3.3.4)
Requires-Dist: numpy (==1.19.5)
Requires-Dist: PyLaTeX (==1.4.1)
Requires-Dist: pyyaml (==5.4.1)
Requires-Dist: scikit-learn (==0.24.2)
Requires-Dist: scipy (==1.5.4)
Requires-Dist: statsmodels (==0.12.2)
Requires-Dist: tensorflow (==2.5.0)
Requires-Dist: foolbox (==3.3.1)

ML-PePR: Pentesting Privacy and Robustness
==========================================

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.

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

Installation
------------

To install pepr use ``pip install mlpepr``.


