README

This is for releasing the source code of the paper "Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems"

Archived Version: RamBoAttack

The project is published as part of the following paper and if you re-use our work, please cite the following paper:

@inproceedings{vo2022,
title={RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit},
author={Viet Quoc Vo and Ehsan Abbasnejad and Damith C. Ranasinghe},
year = {2022},
journal = {Network and Distributed Systems Security (NDSS) Symposium},
}

The source code is written mostly on Python 3 and Pytorch, so please help to download and install Python3 and Pytorch beforehand.

Requirements

To install the requirements for this repo, run the following command:

git clone https://github.com/RamBoAttack/RamBoAttack.github.io.git
cd RamBoAttack
pip3 install -r requirements.txt

Run the RamBoAttack

There are two ways to run the method:

• The first way is to run step-by-step with the Jupyter Notebook file RamBoAttack.ipynb in the main folder.

• The second way is to run the test.py file. This is to run the RamBoAttack on the whole test set for that task.:

# For example, to run RamBoAttack on CIFAR10
cd main
python3 test.py

TODO

• add the testing code.

Notes:

1. The pretrained model for CIFAR-10 can be downloaded from this repo.