Deep Learning-based Detection Of Skilled Signature Forgeries

Abstract

Signature Verification Plays A Critical Role In Authentication Systems Used In Banking, Legal Documentation, And Financial Transactions. However, Skilled Signature Forgeries Pose A Significant Challenge For Traditional Verification Techniques. This Paper Presents A Deep Learning-based Approach For Detecting Skilled Signature Forgeries Using A Convolutional Neural Network (CNN). The Proposed System Compares An Original Signature With A Suspected Signature And Determines Whether The Signature Is Genuine Or Forged. The Model Is Implemented Using The PyTorch Deep Learning Framework And Deployed Through A Flask-based Web Application. Image Preprocessing Techniques Such As Resizing, Grayscale Conversion, And Normalization Are Applied Before Feeding The Signatures Into The CNN Model. The System Extracts Discriminative Features From Signature Images Through Multiple Convolutional Layers And Predicts The Authenticity Of The Signature With A Confidence Score. Experimental Results Demonstrate That The Proposed Approach Effectively Identifies Forged Signatures And Provides Reliable Verification Performance. The Developed System Can Assist In Preventing Fraud In Financial And Authentication Systems.

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