轻量模型+对抗测试，提升可穿戴心电生物识别安全性
Lightweight MobileNetV1+GRU for ECG Biometric Authentication: Federated and Adversarial Evaluation
- 用MobileNetV1+GRU架构压缩模型，适配可穿戴设备实时处理
- 在4个数据集上准确率超98%，对抗攻击下性能仍保持稳定
- 首次融合联邦学习与对抗评估，适合隐私敏感的医疗应用
心电生物识别具有独特安全性，但部署于可穿戴设备时面临实时性、隐私和伪造攻击挑战。本文提出轻量级深度学习模型MobileNetV1+GRU，结合20dB高斯噪声注入与定制预处理，在ECGID、MIT-BIH、CYBHi和PTB数据集上实现99.34%、99.31%、91.74%和98.49%的准确率，F1分数分别为0.9869、0.9923、0.9125、0.9771，等错误率（EER）低至0.0009、0.00013、0.0091、0.0009，ROC-AUC达0.9999、0.9999、0.9985、0.9998。在FGSM对抗攻击下，准确率从96.82%降至最低0.80%。研究强调联邦学习与对抗测试的重要性，并呼吁构建多样化可穿戴生理数据集以保障生物识别的安全与可扩展性。
原文摘要 · Abstract (English)
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.
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