MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors

Granese et al. "MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26409-2_18

[Granese et al. "MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/granese2022ecmlpkdd-mead/) doi:10.1007/978-3-031-26409-2_18

@inproceedings{granese2022ecmlpkdd-mead,
  title     = {{MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors}},
  author    = {Granese, Federica and Picot, Marine and Romanelli, Marco and Messina, Francesco and Piantanida, Pablo},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {286-303},
  doi       = {10.1007/978-3-031-26409-2_18},
  url       = {https://mlanthology.org/ecmlpkdd/2022/granese2022ecmlpkdd-mead/}
}