Deep Learning-Powered Signature Authentication: The SigVerify CNN Model

Verification of signatures is critical for authentication procedures in the legal, banking, and financial domains. A cutting-edge, AI-powered technology called SigVerify Project uses deep learning methods to authenticate handwritten signatures. This study investigates the use of TensorFlow and structural similarity index measurement (SSIM) to create a Convolutional Neural Network (CNN) model for signature verification, ensuring precise matching. A user-friendly interface created using Tkinter makes it possible for users to upload or capture signatures for validation against a dataset that has been stored. Real and fake signatures are used to train the model so that it can accurately differentiate between authentic and fraudulent attempts. In order to improve model performance, the methodology uses a dataset of 3000 images, training-test split (80:20 ratio), k-fold cross-validation, and hyperparameter adjustment. Furthermore, the robustness of the model is enhanced via data augmentation methods including noise injection and rotation. False Acceptance Rate (FAR), False Rejection Rate (FRR), accuracy, and F1-score are used to evaluate performance. Verification accuracy is 94.8%, according to experimental data. This research offers a secure and efficient method for automated signature verification, contributing to the continuous improvements in biometric security and fraud prevention.