Code for the implementation of the paper Adversarial Attacks on Variational Autoencoders - George Gondim-Ribeiro, Pedro Tabacof, Eduardo Valle, 2018.
Please cite our work:
George Gondim-Ribeiro, Pedro Tabacof, and Eduardo Valle. Adversarial Attacks on Variational Autoencoders. arXiv:1806.04646.
After downloading and preparing the data, to reproduce our experiments, first, run the script trainer.py to train a model and the attack.py to start the attack choosing the same directory. Then, you can compute the AUDDC on the normalized results.
For instance:
# Download data
python get_data.py
# Prepare CelebA dataset
python celeba_prepare_data.py
# Train model
python trainer.py --epochs 50 --dataset mnist --architecture vae --dir /tmp/test
# Attack model
python attack.py --dir /tmp/test/ --num_attacks 5
# Compute metrics
python compute_metrics.py --dir /tmp/test
vae: Variational autoencoders with only fully-connected layers
cvae: Variational autoencoders with convolutional layers
draw: DRAW
mnist: MNIST dataset
svhn: SVHN dataset
celeba: CelebA dataset
This code is based upon the repositories below, please check their license before any use. This code is intended ONLY for academic purposes.