An efficient transfer learning model for predicting forged (handwritten) signature

Signature fraud around the world is increasing at an alarming rate. Fraud in the signature may harm a person or an organization by false transactions and false document authorization, which may lead to an irreversible loss. Thus, the purpose of this research is to predict forged signature using machine learning techniques. To attain the objective, different state-of-the-art machine learning models, including Neural Network, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest Classifier, were developed to classify between fraud and real signatures. The VGG-16 pre-trained model was used to improve the Neural Network’s performance. As outcome, the transfer learning based Neural Network model showed the highest accuracy-96.7%, followed by Support Vector Machine (81.7%), K-Nearest Neighbors (71.7%), Random Forest (70.0%), and Decision Tree (68.3%).

Recommended citation: M. R. Sheikh, T. H. Masud, N. I. Khan and M. N. Islam, "An Efficient Transfer Learning Model for Predicting Forged (Handwritten) Signature," 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 2021, pp. 1-4, doi: 10.1109/IC4ME253898.2021.9768440.
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