DETECTING SIGNATURE FORGERY USING CONVOLUTIONAL NEURAL NETWORKS

Handwritten signatures remain a widely accepted form of identity verification in financial, legal, governmental,and institutional settings. However, they are highly vulnerable to forgery, particularly in offline scenarios wheredynamic behavioral traits like pen pressure, speed, and stroke order are unavailable. Traditional methods,including manual verification by forensic experts and rule-based automated systems, suffer from subjectivity,limited scalability, and poor performance against skilled forgeries.This research presents a Convolutional Neural Network (CNN)-based framework for offline signature forgerydetection that leverages deep learning to extract discriminative features directly from static signature images.The proposed system incorporates robust preprocessing techniques, advanced data augmentation to address classimbalance, and transfer learning using state-of-the-art architectures like ResNet, VGG, EfficientNet, andMobileNetV2. To improve transparency and legal applicability, the system integrates visual interpretabilitytools such as Grad-CAM and saliency maps to highlight regions influencing model decisions.Experiments conducted on five benchmark datasets—GPDS, CEDAR, BHSig260, UTSig, and MCYT—demonstrate the superior performance of the proposed model over traditional machine learning approaches. TheCNN-based system achieved accuracy above 96% on all datasets, with Equal Error Rates (EER) as low as 1.7%.It also supports mobile and real-time deployment, making it viable for use in banking kiosks, forensic tools, anddigital identity platforms.