Universal Adversarial Perturbations in Epileptic Seizure Detection
Aminifar, Amir LU
(2020)
2020 International Joint Conference on Neural Networks, IJCNN 2020
- Abstract
Adversarial examples have received a lot of attention over the past decade, particularly with the rise of deep neural networks. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences, involving patients' lives. In this article, we consider adversarial perturbations in the context of medical and health applications and focus on the epileptic seizure detection problem. We formulate an optimization problem for computing universal adversarial perturbations and show that such universal perturbations may be used to declare the majority of seizure samples as non-seizure, i.e., to fool the classification algorithm, while being imperceptible... (More)
Adversarial examples have received a lot of attention over the past decade, particularly with the rise of deep neural networks. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences, involving patients' lives. In this article, we consider adversarial perturbations in the context of medical and health applications and focus on the epileptic seizure detection problem. We formulate an optimization problem for computing universal adversarial perturbations and show that such universal perturbations may be used to declare the majority of seizure samples as non-seizure, i.e., to fool the classification algorithm, while being imperceptible to the medical expert eye.
(Less)
- author
- Aminifar, Amir
LU
- publishing date
- 2020-07
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Epileptic Ictal Activity, Epileptic Seizure Detection, Universal Adversarial Perturbation
- host publication
- 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
- article number
- 9206696
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2020 International Joint Conference on Neural Networks, IJCNN 2020
- conference location
- Virtual, Glasgow, United Kingdom
- conference dates
- 2020-07-19 - 2020-07-24
- external identifiers
-
- scopus:85093819284
- ISBN
- 9781728169262
- DOI
- 10.1109/IJCNN48605.2020.9206696
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2020 IEEE.
- id
- ceb5b18d-4c41-4473-aebc-4e2d0b680508
- date added to LUP
- 2022-02-05 01:17:06
- date last changed
- 2022-04-22 07:42:14
@inproceedings{ceb5b18d-4c41-4473-aebc-4e2d0b680508,
abstract = {{<p>Adversarial examples have received a lot of attention over the past decade, particularly with the rise of deep neural networks. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences, involving patients' lives. In this article, we consider adversarial perturbations in the context of medical and health applications and focus on the epileptic seizure detection problem. We formulate an optimization problem for computing universal adversarial perturbations and show that such universal perturbations may be used to declare the majority of seizure samples as non-seizure, i.e., to fool the classification algorithm, while being imperceptible to the medical expert eye.</p>}},
author = {{Aminifar, Amir}},
booktitle = {{2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings}},
isbn = {{9781728169262}},
keywords = {{Epileptic Ictal Activity; Epileptic Seizure Detection; Universal Adversarial Perturbation}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Universal Adversarial Perturbations in Epileptic Seizure Detection}},
url = {{http://dx.doi.org/10.1109/IJCNN48605.2020.9206696}},
doi = {{10.1109/IJCNN48605.2020.9206696}},
year = {{2020}},
}