NIPS Adversarial Vision Challenge
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README.md

NIPS Adversarial Vision Challenge

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Publication

https://arxiv.org/abs/1808.01976

Installation

To install the package simply run:

pip install adversarial-vision-challenge

This package contains helper functions to implement models and attacks that can be used with Python 2.7, 3.4, 3.5 and 3.6. Other Python versions might work as well. We recommend using Python 3!

Furthermore, this package also contains test scripts that should be used before submission to perform local tests of your model or attack. These test scripts are Python 3 only, because they depend on crowdai-repo2docker. See Running Tests Scripts section below for more detailed information.

Implementing a model

To run a model server, load your model and wrap it as a foolbox model. Then pass the foolbox model to the model_server function.

from adversarial_vision_challenge import model_server

foolbox_model = load_your_foolbox_model()
model_server(foolbox_model)

Implementing an attack

To run an attack, use the load_model method to get a model instance that is callable to get the predicted labels.

from adversarial_vision_challenge.utils import read_images, store_adversarial
from adversarial_vision_challenge.utils import load_model

model = load_model()

for (file_name, image, label) in read_images():
    # model is callable and returns the predicted class,
    # i.e. 0 <= model(image) < 200

    # run your adversarial attack
    adversarial = your_attack(model, image, label)

    # store the adversarial
    store_adversarial(file_name, adversarial)
    
 ### Running the tests scripts

Running Tests Scripts

The test scripts (running on your host machine) will need Python 3. Your model or attack running inside a docker container and using this package can use Python 2 or 3.

  • To test a model, run the following: avc-test-model .
  • To test an untargeted attack, run the following: avc-test-untargeted-attack .
  • To test an targeted attack, run the following: avc-test-targeted-attack .

within the folders you want to test.

In order for the attacks to work, your models / attack folders need to have the following structure:

FAQ

Can you recommend some papers to get more familiar with adversarial examples, attacks and the threat model considered in this NIPS competition?

Have a look at our reading list that summarizes papers relevant for this competition.

How can I cite the competition in my own work?

@inproceedings{adversarial_vision_challenge,
title = {Adversarial Vision Challenge},
author = {Brendel, Wieland and Rauber, Jonas and Kurakin, Alexey and Papernot, Nicolas and Veliqi, Behar and Salath{\'e}, Marcel and Mohanty, Sharada P and Bethge, Matthias},
booktitle = {32nd Conference on Neural Information Processing Systems (NIPS 2018) Competition Track},
year = {2018},
url = {https://arxiv.org/abs/1808.01976}
}

Why can I not pass bounds = (0, 1) when creating the foolbox model?

We expect that all models process images that have values between 0 and 255. Therefore, we enforce that the model bounds are set to (0, 255). If your model expects images with values between 0 and 1, you can just pass bounds=(0, 255) and preprocessing=(0, 255), then the Foolbox model wrapper will divide all inputs by 255. Alternatively, you can leave preprocessing at (0, 1) and change your model to expect values between 0 and 255.

How is the score for an individual model, attack, image calculated?

We normalize the pixel values of the clean image and the adversarial to be between 0 and 1 and then take the L2 norm of the perturbation (adverarial - clean) as if they were vectors.