Model Fooling Attacks Against Medical Imaging: A Short Survey

This study aims to find a list of methods to fool artificial neural networks used in medical imaging. We collected a short list of publications related to machine learning model fooling to see if these methods have been used in the medical imaging domain. Specifically, we focused our interest to pathological whole slide images used to study human tissues. While useful, machine learning models such as deep neural networks can be fooled by quite simple attacks involving purposefully engineered images. Such attacks pose a threat to many domains, including the one we focused on since there have been some studies describing such threats.

Authors

Tuomo Sipola, Samir puuska, Tero Kokkonen

Cite as

Sipola, Tuomo, Samir Puuska, and Tero Kokkonen. “Model Fooling Attacks Against Medical Imaging: A Short Survey.” Information & Security: An International Journal 46, no. 2 (2020): 215-224. https://doi.org/10.11610/isij.4615

DOI

https://doi.org/10.11610/isij.4615

Acknowledgments

This research is partially funded by the Cyber Security Network of Competence Centres for Europe (CyberSec4Europe) project of the Horizon 2020 SU-ICT-03- 2018 program.

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