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Adversarial Robustness Test Bed

Project description


GitHub CI PyPI Status Badge PyPI - Python Version License: MIT Code style: black

ARMORY is a test bed for running scalable evaluations of adversarial defenses. Configuration files are used to launch local or cloud instances of the ARMORY docker container. Models, datasets, and evaluation scripts can be pulled from external repositories or from the baselines within this project.

Installation

pip install armory-testbed

Upon installing armory, a directory will be created at ~/.armory. This user specific folder is the default directory for downloaded datasets and evaluation outputs. Defaults can be changed by editing ~/.armory/config.json

Usage

ARMORY works by running an evaluation configuration file within the armory docker ecosystem. To do this, simply run armory run <path_to_evaluation.json>. Please see example configuration files for runnable configs.

The current working directory and armory installation directory will be mounted inside the container and the armory.eval.Evaluator class will proceed to run the evaluation script that is written in the evaluation['eval_file'] field of the config.

For more detailed information on the evaluation config file please see the documentation.

Note: Since ARMORY launches Docker containers, the python package must be ran on system host.

As an example:

pip install armory-testbed
git clone https://github.com/twosixlabs/armory-example.git
cd armory-example
armory run example_config.json

Interactive Debugging of Evaluations

Debugging evaluations can be performed interactively by passing --interactive and following the instructions to attach to the container in order to use pdb or other interactive tools. There is also support for --jupyter which will open a port on the container and allow notebooks to be ran inside the armory environment.

Custom Attacks and Defenses

Our evaluations are created so that attacks and defenses may be interchanged. To do this we standardize all attacks and defenses as subclasses of their respective implementations in adversarial-robustness-toolbox

Docker

Armory is intended to be a lightweight python package which standardizes all evaluations inside a docker container. Docker images will be pulled as needed when evaluations are ran.

However if there are issues downloading the images (e.g. proxy) they can be built within this repo:

version=$(python -c "import armory; print(armory.__version__)")
docker build --build-arg armory_version=${version} --target armory-tf1 -t twosixarmory/tf1:${version} .
docker build --build-arg armory_version=${version} --target armory-tf2 -t twosixarmory/tf2:${version} .
docker build --build-arg armory_version=${version} --target armory-pytorch -t twosixarmory/pytorch:${version} .

Docker Mounts

By default when launching an ARMORY instance the current working directory will be mounted as your default directory.This enables users to run modules from ARMORY baselines, as well as modules from the user project.

Docker Setup

Depending on the task, docker memory for an ARMORY container must be at least 8 GB to run properly (preferably 16+ GB). On Mac and Windows Desktop versions, this defaults to 2 GB. See the docs to change this:

Docker Cleanup

Running armory download-all-data will download new Docker images, but will not clean up old images.

To download new images and clean up old images:

armory clean

If containers are currently running that use the old images, this will fail. In that case, either stop them with first or run:

armory clean --force

To display the set of current images:

docker images

To manually delete images, see the docs for docker rmi.

In order to see the set of containers that are running:

docker ps

ARMORY will attempt to gracefully shut down all containers it launches; however, certain errors may prevent shutdown and leave running containers. To shut down these containers, please see the docs for docker stop and docker kill.

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