Code for Adversarial Examples in Electrocardiograms
The code is written in python 3.6 and pytorch 1.0.
The data is from https://physionet.org/challenge/2017/ It is read by python and saved in .npy file.
The signals file is too large. It cannot be uploaded to github. This is the Google drive link: https://drive.google.com/file/d/1u10ZvEilpZnOB4ls5ZAU5ywwf9g23oiB/view?usp=sharing
Before you use the code, please download raw_data.npy from the link above and put it into the data folder.
In this folder:
- train.py: Train the model and will save best_model.pth in saved_model folder. This training may take two days. One pretrained model is already saved in the saved_model folder.
- create_adv_pgd.py: Generate adversarial examples by traditional attack method PGD.
- create_adv_conv_train.py: Generate adversarial examples by smoothed attack method SAP.
- adv_measure_norm_all.py: Generate new adversarial examples by adding Gaussian noise.
- check_concat.py: Check that the concatenations of the new adversarial examples generated by adv_measure_norm_all.py are still adversarial examples.
- check_uniform.py: Check that uniform samples from the band generated by adv_measure_norm_all.py are still adversarial examples.