Signature Originality Verification Using A Deep Learning Approach - Saputra, Muhammad Azi; Nurhaida, Ida (Electronic Journal of Education, Social Economics and Technology, 2024)
The rapid advancement of digital technology has heightened the need for reliable methods to verify signature authenticity, a critical aspect of document and transaction security. This study uses a deep learning approach to develop a mobile application to verify the originality of paper and digital media signatures. The dataset comprises 1,060 signature images, including authentic and forged categories for both media types. The system employs the EfficientNetV2M model, trained with augmented data, to enhance robustness. Model evaluation demonstrates strong performance with an accuracy of 82.07%, a global precision of 81.31%, a global recall of 83.25%, and a global F1-score of 82.18%. The model is implemented in an Android-based mobile application, providing an intuitive interface for users to upload and verify signatures in real time. These results underscore the potential of EfficientNetV2M for mitigating signature fraud across various domains while highlighting areas for improvement, particularly in classifying paper-based signatures. Future work will focus on expanding the dataset and refining feature extraction techniques to enhance classification performance.