tomginsberg / DeepZAdapt

A learnable L-Infinity Neural Network Verifier based on the DeepZ framework

DeepZAdapt

View the project write up Learning to Hide Zonotopes Behind HyperPlanes

Setup instructions

You can create virtual environment and install the dependencies using the following commands:

$ virtualenv venv --python=python3.6
$ source venv/bin/activate
$ pip install -r requirements.txt

Running the verifier

Run the verifier from code directory using the command:

$ python verifier.py --net {net} --spec ../test_cases/{net}/img{test_idx}_{eps}.txt

In this command, {net} is equal to one of the following values (each representing one of the networks we want to verify): fc1, fc2, fc3, fc4, fc5, conv1, conv2, conv3, conv4, conv5. test_idx is an integer representing index of the test case, while eps is perturbation that verifier should certify in this test case.

To test the verifier, you can run for example:

$ python verifier.py --net fc1 --spec ../test_cases/fc1/img0_0.06000.txt

To evaluate the verifier on all networks and sample test cases, we provide the evaluation script. You can run this script using the following commands:

chmod +x evaluate
./evaluate