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Code for "Black-box Adversarial Attacks with Limited Queries and Information" (http://arxiv.org/abs/1804.08598)
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tools
.gitignore Initial Commit Apr 24, 2018
README.md Update citation May 12, 2018
attacks.py Fix early stopping with untargeted attack Dec 24, 2018
label-only.sh Initial Commit Apr 24, 2018
main.py
partial-info.sh Initial Commit Apr 24, 2018
precompute.py Initial Commit Apr 24, 2018
query-limited.sh Initial Commit Apr 24, 2018

README.md

Code for Black-box Adversarial Attacks with Limited Queries and Information

Codebase for reproducing the results in the paper "Black-Box Adversarial Attacks with Limited Queries and Information". The paper can be found on arxiv, and our explanatory blog post can be found on labsix.org.

To reproduce our results:

  1. Make a directory tools/data, and in it put the decompressed Inceptionv3 classifier from (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
  2. Set IMAGENET_PATH in main.py, attacks.py, and precompute.py to the location of the ImageNet dataset on your machine.
  3. Precompute the starting images (for partial-information and label-only attacks) with python precompute.py
  4. Run the reproduction scripts with {query-limited|partial-info|label-only}.sh, making sure to first edit them specifying an img-index (by default runs on imagenet image 0)

Citation

@inproceedings{ilyas2018blackbox,
  author = {Andrew Ilyas and Logan Engstrom and Anish Athalye and Jessy Lin},
  title = {Black-box Adversarial Attacks with Limited Queries and Information},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning, {ICML} 2018},
  year = {2018},
  month = jul,
  url = {https://arxiv.org/abs/1804.08598},
}
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