Adaptive Neighbourhoods for the Discovery
of Adversarial Examples

Python API for generating adapted and unique neighbourhoods for searching for adversarial examples

Installation & usage

This work is released on PyPi. Installation, therefore, is as simple as installing the package with pip:

python3 -m pip install adaptive-neighbourhoods

At this point, you're free to start generating neighbourhoods for your own dataset:

from adaptive_neighbourhoods import epsilon_expand

neighbourhoods = epsilon_expand(
    x,  # your input data
    y)  # the integer encoded labels for your data

Move information on the variable parameters and general guidance on using this package can be found at: https://adaptive-neighbourhoods.readthedocs.io/en/latest/

Contributing

All contributions and feedback are welcome!

There are three main remote mirrors used for hosting this project. If you would like to contribute, please submit an issue/pull-request/patch-request to any of these mirrors:

• Github: /~https://github.com/jaypmorgan/adaptive-neighbourhoods
• Gitlab: https://gitlab.com/jaymorgan/adaptive-neighbourhoods
• Source Hut: https://git.sr.ht/~jaymorgan/adaptive-neighbourhoods

Citing this work

If you use this work in your research, please consider referencing our article using the following bibtex entry:

@article{DBLP:journals/corr/abs-2101-09108,
  author    = {Jay Paul Morgan and
               Adeline Paiement and
               Arno Pauly and
               Monika Seisenberger},
  title     = {Adaptive Neighbourhoods for the Discovery of Adversarial Examples},
  journal   = {CoRR},
  volume    = {abs/2101.09108},
  year      = {2021},
  url       = {https://arxiv.org/abs/2101.09108},
  eprinttype = {arXiv},
  eprint    = {2101.09108},
  timestamp = {Sat, 30 Jan 2021 18:02:51 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2101-09108.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}