adaptive-neighbourhoods 0.1.2

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

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}
}