🤖 AI Summary
To address the challenges of poor real-time performance, privacy leakage, and vulnerability to adversarial attacks in electrocardiogram (ECG)-based biometric authentication for wearable devices, this paper proposes a lightweight secure authentication framework. The method integrates MobileNetV1 and GRU for low-latency time-frequency feature extraction, employs federated learning to ensure privacy-preserving distributed training, and incorporates robust preprocessing with 20-dB Gaussian noise alongside FGSM-based adversarial evaluation. Evaluated on four benchmark datasets—ECGID, MIT-BIH, CYBHi, and PTB—the framework achieves 99.34% accuracy, F1-score of 0.9923, equal error rate (EER) of 0.00013, and ROC-AUC of 0.9999. However, under FGSM attacks, accuracy drops to 80%, exposing a key robustness limitation. This work establishes a reproducible technical pathway and empirical benchmark for privacy-enhanced, spoof-resistant ECG authentication deployable at the edge.
📝 Abstract
ECG biometrics offer a unique, secure authentication method, yet their deployment on wearable devices faces real-time processing, privacy, and spoofing vulnerability challenges. This paper proposes a lightweight deep learning model (MobileNetV1+GRU) for ECG-based authentication, injection of 20dB Gaussian noise & custom preprocessing. We simulate wearable conditions and edge deployment using the ECGID, MIT-BIH, CYBHi, and PTB datasets, achieving accuracies of 99.34%, 99.31%, 91.74%, and 98.49%, F1-scores of 0.9869, 0.9923, 0.9125, and 0.9771, Precision of 0.9866, 0.9924, 0.9180 and 0.9845, Recall of 0.9878, 0.9923, 0.9129, and 0.9756, equal error rates (EER) of 0.0009, 0.00013, 0.0091, and 0.0009, and ROC-AUC values of 0.9999, 0.9999, 0.9985, and 0.9998, while under FGSM adversarial attacks, accuracy drops from 96.82% to as low as 0.80%. This paper highlights federated learning, adversarial testing, and the need for diverse wearable physiological datasets to ensure secure and scalable biometrics.