Spatially Transformed Adversarial Examples with TensorFlow
Deep neural networks have been shown to be vulnerable to adversarial examples: very small perturbations of the input having a dramatic impact on the predictions. In this package, we provide a TensorFlow implementation for a new type of adversarial attack based on local geometric transformations: Spatially Transformed Adversarial Examples (stAdv).
Our implementation follows the procedure from the original paper:
If you use this code, please cite the following paper for which this implementation was originally made:
Robustness of Rotation-Equivariant Networks to Adversarial PerturbationsBeranger Dumont, Simona Maggio, Pablo Montalvo
First, make sure you have installed TensorFlow (CPU or GPU version).
Then, to install the stadv package, simply run
$ pip install stadv
A typical use of this package is as follows:
- Start with a trained network implemented in TensorFlow.
- 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.
- 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.
- 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).
The documentation of the API is available at http://stadv.readthedocs.io/en/latest/stadv.html.
You can run all unit tests with
$ make init $ make test
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