This is a replication package for the ICSEW'20 paper SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation.
The ipynb files contain code for the experiments done in the SINVAD paper.
They are presented in notebook form to facilitate interactive experimentation.
The subdirectories contain code to train the models that are used in SINVAD experiments.
Set up a python3 environment in your preferred way, then
pip install -r requirements.txt
For each research question's experiments, the prerequisite models are trained using the bash scripts in init_scripts/.
To run RQx.ipynb, you would first run
cd init_scripts
sh init_rqx.sh
where x is replaced with the desired RQ number.
Alternatively, to train models for all the RQs, one can run the all_init.sh script in the same directory.
Set up a Jupyter server and run the notebooks.