A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning
- DOI
- 10.1109/tetci.2020.2968933
- Published
- 2020-08
- Container
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tetci.2020.2968933,
title = {A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning},
author = {Koosha Sadeghi and Ayan Banerjee and Sandeep K. S. Gupta},
year = {2020},
journal = {IEEE Transactions on Emerging Topics in Computational Intelligence},
doi = {10.1109/tetci.2020.2968933},
url = {https://doi.org/10.1109/tetci.2020.2968933}
}RIS
TY - JOUR TI - A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning AU - Koosha Sadeghi AU - Ayan Banerjee AU - Sandeep K. S. Gupta PY - 2020 JO - IEEE Transactions on Emerging Topics in Computational Intelligence DO - 10.1109/tetci.2020.2968933 UR - https://doi.org/10.1109/tetci.2020.2968933 ER -
APA
Sadeghi, K., Banerjee, A., & Gupta, S. K. S. (2020). A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/tetci.2020.2968933
Source records
- crossref · retrieved 2026-09-25T13:17:58.501Z