trojai 0.2.7

TrojAI model and dataset generation library

Overview

This is the top-level TrojAI module. It contains two submodules: datagen and modelgen. datagen contains the necessary API functions to quickly generate synthetic data that could be used for training machine learning models. The modelgen module contains the necessary API functions to quickly generate DNN models from the generated data.

Installation

Any necessary Python dependencies are captured in the requirements.txt file. It is recommended to install the dependencies into a dedicated virtual environment. After creating and activating a virtual environment via Anaconda or Python3, the commmand pip install -r requirements.txt will setup the environment with the necessary dependencies required.

Getting Started

1. Check the datagen README to learn more about the data generation API.
2. Check the datagen scripts directory for examples on using the data generation API to generate data.
3. Check the modelgen README to learn more about the model generation API.
4. Check the modelgen scripts directory for examples on using the model generation API to generate models.

Repository Organization

trojai
|   setup.py - Script to install trojai module into Python environment
|   requirements.txt - A list of Python dependencies for pip
│   developers - information for developers
│   scripts
    └───datagen - integration scripts showcasing datagen API functionality
    └───modelgen - integration scripts showcasing modelgen API functionality
└───trojai - top level Python module
    └───datagen - data generation submodule
    └───modelgen - data generation submodule
    └───test - top level scripts directory
        └───datagen - contains unittests for the datagen submodule
        └───modelgen - contains unittests for the modelgen submodule

Acknowledgements

This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.