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How to attack PPG biometric using adversarial machine learning
Author(s): Nima Karimian
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Paper Abstract

Wearable technology is growing exponentially and becoming as part of our life where daily activities will be tracked and monitored. As more and more wearable device are connected to the internet, the more urgent the need for authentication is required. Biometrics is rapidly gaining popularity as a powerful authenticator to meet this challenge. Biometric technology enables users to identify themselves quickly and securely. However, because of the nature of the IoT device, there is a need for continuous authentication. Cardiovascular biometric technology such as ECG and PPG are already moving forward as a biometric continuous authentication. However, it was recently shown that an ECG signal is vulnerable to presentation attack. Since PPG is widely used in wearable devices, they are more vulnerable to presentation attack. In this paper, we introduce a systematic presentation attack on PPG biometric where a short template of the victim’s PPG is collected by an attacker and used to map the adversarial’s PPG into the victims.

Paper Details

Date Published: 2 May 2019
PDF: 7 pages
Proc. SPIE 11009, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019, 1100909 (2 May 2019); doi: 10.1117/12.2518828
Show Author Affiliations
Nima Karimian, San José State Univ. (United States)


Published in SPIE Proceedings Vol. 11009:
Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019
Michael C. Dudzik; Jennifer C. Ricklin, Editor(s)

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