GUARDIAN GRAPH: ENHANCING SECURITY WITH HANDWRITTEN SIGNATURE VERIFICATION
Keywords:
Guardian Graph, Handwritten signature verification, SecurityAbstract
Guardian Graph is a novel model designed for enhancing security through handwritten signature verification. With the proliferation of digital transactions, ensuring the authenticity of signatures has become paramount for preventing fraud and unauthorized access. Guardian Graph employs advanced machine learning techniques to analyze and authenticate handwritten signatures, leveraging graph-based approaches for feature extraction and classification. By comparing the structural and behavioral characteristics of signatures, Guardian Graph achieves high accuracy and reliability in verification tasks. This paper presents the architecture, methodology, and evaluation results of Guardian Graph, highlighting its effectiveness in bolstering security measures in various applications.
Downloads
References
Abikoye, O., Mabayoje, M., and Ajibade, R., 2011. Offline signature recognition & verification using neural network. International Journal of Computer Applications, 35 (2), 44–51.
Adeyemo, A. and Abiodun, A., 2015. Adaptive SIFT/SURF algorithm for off-line signature recognition. Journal of Egyptian Computer Science, 39 (1), 50–56.
Bay, H., et al., 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding Archive, 110 (3), 346–359.
Biradar, S. and Panchal, S., 2015. Bank cheque identification and classification using ANN. International Journal Of Engineering And Computer Science, 4 (7), 13237–13242.
Celar, S., et al., 2015. Classification of test documents based on handwritten student ID’s characteristics. In Proceeding: 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, Procedia Engineering, 100 (1), 782–790.
Chambers, J., et al., 2015. Currency security and forensics: a survey. Multimedia Tools and Applications, 74 (11), 4013–4043.
Das, S. and Roy, A., 2015. Signature verification using rough set theory based feature selection. Advances in Intelligent Systems and Computing, 411, 153–161.
Dhaka, V., Rao, M., and Manu Singh, P., 2009. Signature verification on bank checks using Hopfield neural network. KARPAGAM Journal of Computer Science, 3 (4), 9.
Galbally, J., et al., 2015. On-line signature recognition through the combination of real dynamic data and synthetically generated static data. Pattern Recognition, 48 (9), 2921–2934.
Gupta, S., 2014. Handwritten signature verification using artificial neural network. International Journal of Modern Trends in Engineering and Research, 1 (2), 308–322. ISSN:2349-9745.
Hafemann, L., Sabourin, R., and Oliveira, L., 2015. Offline handwritten signature verification – literature review. Computer Vision and Pattern Recognition, Submitted on 28 Jul 2015 (v1), last revised 19 Aug 2015.
Halder, B., et al., 2014. Analysis of fluorescent paper pulps for detecting counterfeit Indian paper money. Information Systems Security, Lecture Notes in Computer Science, 8880, 411–424.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Mostafa Al-Khatib
This work is licensed under a Creative Commons Attribution 4.0 International License.
The content published on the International Scientific and Current Research Conferences platform, including conference papers, abstracts, and presentations, is made available under an open-access model. Users are free to access, share, and distribute this content, provided that proper attribution is given to the original authors and the source.
