Robust Perception Through Analysis By Synthesis
The ABS model is a robust (w.r.t. Adversarial Examples) classifier on MNIST. For more details checkout our paper "Towards the first adversarially robust neural network model on MNIST https://arxiv.org/abs/1805.09190 .
This code provides the pre-trained ABS models and baselines such as: a vanilla CNN, a binary CNN, a Nearest Neighbour classifier, the model of Madry et al.  and our Analysis by Synthesis (ABS) model.
A random selection of adversarial examples for the different models can be seen below.
To generate adversarial examples and run the code agnostic of the deeplearning framework (e.g. tensorflow, torch), we use foolbox . Foolbox support decision-, score- and gradient-based attacks. For gradient-based attacks, the gradients can either be calculated directly or estimated with the model scores and finite difference based methods. Additionally some model specific attacks (LatentDescent) are provided.
Lastly we also compute distal (also called trash) adversarial examples which are unrecognizabale images which are classified with high confidence.
Loading the ABS Model
The model can be loaded and supports the standard pytorch API
from abs_models import models as mz # model zoo from abs_models import utils as u model = mz.get_VAE(n_iter=50) # ABS do n_iter=1 for speedup (but ess accurate) batch, label = u.get_batch() # returns torch.tensor, shape (batch_size, n_channels, nx, ny) logits = model(u.n2t(batch))
For a complete example using foolbox see "scripts/attacks.ipynb" or "scripts/attacks.py".
With the power of foolbox one can also run a code agnostic version of the model
Our code used pytorch and python3.6 and can be found here (this repo):
git clone https://github.com/lukas-schott/AnalysisBySynthesis.git
The dependencies are:
pip3 --no-cache-dir install \ numpy \ http://download.pytorch.org/whl/cu90/torch-0.4.0-cp36-cp36m-linux_x86_64.whl \ torchvision \ foolbox \
Have fun :).
 Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel. Towards the first adversarially robust neural network model on MNIST. 2018. URL https://arxiv.org/abs/1805.09190
 Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. URL https://openreview.net/forum?id=rJzIBfZ
 Jonas Rauber and Wieland Brendel. Foolbox Documentation. Read the Docs, 2018. URL https://media.readthedocs.org/pdf/foolbox/latest/foolbox.pdf