Circumventing ECG Authentication with Deep Generative Models based on PPG Pulse Data

Electrocardiogram (ECG) biometrics is a steadily growing and increasingly popular field of research. In this work, we propose a novel attack scenario in which we train a generative model to uncover and spoof the ECG of a victim by merely observing another cardiovascular signal of the victim: their photoplethysmogram (PPG). For the model, we propose a conditional generative adversarial network (cGAN) with a U-Net style generator and least-squares loss. Since current training datasets do not fall into the off-the-person category, we additionally collect a custom dataset of synchronized PPG and ECG measurements. It features 33 recordings by 31 participants with a median age of 28.

We evaluate the model against a baseline by Zhu et al. Our model has a lead over the baseline with a mean relative root-mean-square error (rRMSE) of 0.47 vs. 0.49 on the TBME-RR dataset but lacks behind on our own dataset with a mean rRMSE of 0.61 vs. 0.55. The evaluation demonstrates that the cGAN is able to properly recreate the overall characteristics and noise of the ground truth. In the proposed attack scenario, the model yields an overall success rate of up to 26 % against a neural-network-based authentication system.