How to attack PPG biometric using adversarial machine learning

Nima Karimian

doi:10.1117/12.2518828

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2 May 2019 How to attack PPG biometric using adversarial machine learning

Nima Karimian

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Proceedings Volume 11009, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019; 1100909 (2019) https://doi.org/10.1117/12.2518828
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, Maryland, United States

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.

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Nima Karimian "How to attack PPG biometric using adversarial machine learning", Proc. SPIE 11009, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019, 1100909 (2 May 2019); https://doi.org/10.1117/12.2518828

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