Dataset fingerprinting

This repository contains our proof-of-concept for fingerprinting a dataset.

Local installation

git clone https://github.com/trailofbits/datasig && cd datasig
python3 -m pip install .

Usage

The code below shows experimental usage of the library. This will be subject to frequent changes in early development stages.

from torchvision.datasets import MNIST
from datasig.dataset import TorchVisionDataset, CanonicalDataset

torch_dataset = MNIST(root="/tmp/data", train=True, download=True)
dataset = TorchVisionDataset(torch_dataset)
canonical = CanonicalDataset(dataset)
print("Dataset UID: ", canonical.uid)
print("Dataset fingerprint: ", canonical.fingerprint)

Development

Unit tests

Tests are in the datasig/test directory. You can run the tests with:

python3 -m pytest # Run all tests
python3 -m pytest -s datasig/test/test_csv.py # Run only one test file
python3 -m pytest -s datasig/test/test_csv.py -k test_similarity # Run only one specific test function

Profiling

The profiling script generates a profile for dataset processing and fingerprint generation using cProfile. To profile the MNIST dataset from the torch framework, you can run:

python3 profiling.py torch_mnist --full

The --full argument tells the script to include dataset canonization, UID generation, and fingerprint generation in the profile. If you want to profile only some of these steps you can cherry pick by using or omitting the following arguments instead:

python3 profiling.py torch_mnist --canonical --uid --fingerprint

You can optionally specify the datasig config version to use (at the time of writing we have only v0) with:

python3 profiling.py torch_mnist -v 0 --all

Currently we support only one target dataset: torch_mnist. To add another dataset, add a class in profiling.py similar to TorchMNISTV0, that implements the _setup() method which is responsible for loading the dataset.

Benchmarking

Datasig has a built-in benchmark module that allows to run experiments to benchmark speed and accuracy of various fingerprinting methods with varying configurations and on several datasets.

Benchmarks are configured programmatically using the datasig library directly. The benchmarking.py script gives a comprehensive overview of how to configure and run a benchmark, export results, as well as plot them on graph.

You can run the example benchmark with

python3 benchmarking.py