stAdv: Spatially Transformed Adversarial Examples with TensorFlow

Deep neural networks have been shown to be vulnerable to adversarial examples <https://blog.openai.com/adversarial-example-research/>: very small perturbations of the input having a dramatic impact on the predictions. In this package, we provide a TensorFlow <https://www.tensorflow.org/> implementation for a new type of adversarial attack based on local geometric transformations: Spatially Transformed Adversarial Examples (stAdv).

.. image:: illustration-stadv-mnist.png

Our implementation follows the procedure from the original paper:

| Spatially Transformed Adversarial Examples
| Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song
| `ICLR 2018 (conference track) <https://openreview.net/forum?id=HyydRMZC->`_, `arXiv:1801.02612 <https://arxiv.org/abs/1801.02612>`_

If you use this code, please cite the following paper for which this implementation was originally made:

| Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
| Beranger Dumont, Simona Maggio, Pablo Montalvo
| `ICML 2018 Workshop on "Towards learning with limited labels: Equivariance, Invariance, and Beyond" <https://sites.google.com/site/icml18limitedlabels>`_, `arXiv:1802.06627 <https://arxiv.org/abs/1802.06627>`_

Installation

First, make sure you have installed TensorFlow <https://www.tensorflow.org/install/>_ (CPU or GPU version).

Then, to install the stadv package, simply run

.. code-block:: bash

$ pip install stadv

Usage

A typical use of this package is as follows:

1. Start with a trained network implemented in TensorFlow.
2. Insert the stadv.layers.flow_st layer in the graph immediately after the input layer. This is in order to perturb the input images according to local differentiable geometric perturbations parameterized with input flow tensors.
3. In the end of the graph, after computing the logits, insert the computation of an adversarial loss (to fool the network) and of a flow loss (to enforce local smoothness), e.g. using stadv.losses.adv_loss and stadv.losses.flow_loss, respectively. Define the final loss to be optimized as a combination of the two.
4. Find the flows which minimize this loss, e.g. by using an L-BFGS-B optimizer as conveniently provided in stadv.optimization.lbfgs.

An end-to-end example use of the library is provided in the notebook demo/simple_mnist.ipynb (see on GitHub <demo/simple_mnist.ipynb>_).

Documentation

The documentation of the API is available at http://stadv.readthedocs.io/en/latest/stadv.html.

Testing

You can run all unit tests with

.. code-block:: bash

$ make init
$ make test