1-D CNN-Based Online Signature Verification with Federated Learning
This addresses privacy concerns in security infrastructures for online signature verification, though it appears incremental by combining existing methods.
The paper tackles the problem of data privacy risks in online signature verification by proposing a novel federated learning framework using 1-D CNNs, achieving an EER of 3.33% and accuracy of 96.25% in centralized settings, with EERs ranging from 5.42% to 5.63% in federated configurations.
Online signature verification plays a pivotal role in security infrastructures. However, conventional online signature verification models pose significant risks to data privacy, especially during training processes. To mitigate these concerns, we propose a novel federated learning framework that leverages 1-D Convolutional Neural Networks (CNN) for online signature verification. Furthermore, our experiments demonstrate the effectiveness of our framework regarding 1-D CNN and federated learning. Particularly, the experiment results highlight that our framework 1) minimizes local computational resources; 2) enhances transfer effects with substantial initialization data; 3) presents remarkable scalability. The centralized 1-D CNN model achieves an Equal Error Rate (EER) of 3.33% and an accuracy of 96.25%. Meanwhile, configurations with 2, 5, and 10 agents yield EERs of 5.42%, 5.83%, and 5.63%, along with accuracies of 95.21%, 94.17%, and 94.06%, respectively.