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Minimal Adversarial Perturbations in Mobile Health Applications : The Epileptic Brain Activity Case Study

Aminifar, Amir LU orcid (2020) 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2020-May. p.1205-1209
Abstract

Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. 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 demonstrate the power of such adversarial attacks based on a real-world epileptic seizure detection problem. We identify the minimum perturbation required by the adversaries to declare a seizure (ictal) sample as non-seizure (inter-ictal) in emergency situations, i.e., minimal adversarial perturbation to fool the classification algorithm.

Please use this url to cite or link to this publication:
author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Adversarial Perturbation, Epilepsy, Mobile Health, Privacy and Security, Seizure Detection
host publication
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
series title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
volume
2020-May
article number
9053706
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
conference location
Barcelona, Spain
conference dates
2020-05-04 - 2020-05-08
external identifiers
  • scopus:85089227467
ISSN
1520-6149
ISBN
9781509066315
DOI
10.1109/ICASSP40776.2020.9053706
language
English
LU publication?
no
additional info
Publisher Copyright: © 2020 IEEE.
id
b355de30-cfd2-4fc8-9858-cfaec10a4a07
date added to LUP
2022-02-05 01:18:32
date last changed
2022-04-22 07:26:04
@inproceedings{b355de30-cfd2-4fc8-9858-cfaec10a4a07,
  abstract     = {{<p>Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. 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 demonstrate the power of such adversarial attacks based on a real-world epileptic seizure detection problem. We identify the minimum perturbation required by the adversaries to declare a seizure (ictal) sample as non-seizure (inter-ictal) in emergency situations, i.e., minimal adversarial perturbation to fool the classification algorithm.</p>}},
  author       = {{Aminifar, Amir}},
  booktitle    = {{2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings}},
  isbn         = {{9781509066315}},
  issn         = {{1520-6149}},
  keywords     = {{Adversarial Perturbation; Epilepsy; Mobile Health; Privacy and Security; Seizure Detection}},
  language     = {{eng}},
  pages        = {{1205--1209}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}},
  title        = {{Minimal Adversarial Perturbations in Mobile Health Applications : The Epileptic Brain Activity Case Study}},
  url          = {{http://dx.doi.org/10.1109/ICASSP40776.2020.9053706}},
  doi          = {{10.1109/ICASSP40776.2020.9053706}},
  volume       = {{2020-May}},
  year         = {{2020}},
}