cyberneid/real-forge-signature-detection

Real-Forge-Signature-Detection

In this project we have implemented a deep learning research paper it can be found here

1. Introduction

What is Signature verification and forgery detection ?

• Signature verification and forgery detection is the process of verifying signatures automatically and instantly to determine whether the signature is real or not. There are two main kinds of signature verification: static and dynamic. Static, or offline verification is the process of verifying a document signature after it has been made, while dynamic or online verification takes place as a person creates his/her signature on a digital tablet or a similar device. The signature in question is then compared to previous samples of that person's signature, which set up the database. In the case handwritten signature on a document, the computer needs the samples to be scanned for investigation, whereas a digital signature which is already stored in a data format can be used for signature verification. Handwritten signature is one of the most generally accepted personal attributes for verification with identity whether it may for banking or business.

2. Importance of signature verification

• Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. It is essential in preventing falsification of documents in numerous financial, legal, and other commercial settings. This is a comparative analysis of different already known deep learning architectures to check which of those performs the best on the classification. It was solely for offline handwritten signatures.

3. Datasets Used :

The datasets are not available publicly . But you can mail the publisher and they would provide the download links orelse you can you the dataset available on kaggle.

4. Proposed Methodology

• The handwritten signature is a behavioral biometric which is not based on any physiology characteristics of the individual signature but on the behavior that change over time. Since an individual's signature alters over time the verification and authentication for the signature may take a long period which includes the errors to be higher in some cases. Inconsistent signature leads to higher false rejection rates for an individual· who did not sign in a consistent way.

5. Training The Model

• In this application, we use CNNs(or ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analyzing visual imagery. CNNs were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. In our work,

6. Implementation

• In this work, the signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN has been implemented in python using the Keras with the TensorFlow backend as suggested in the research paper to learn the patterns associated with the signature.

8. Model Architecture

9.RESULTS

• In this implementation we were able to 95.5% accuracy on our validation dataset with 10 epochs

10. Web App Demo

11. Motivation

• A model that can learn from signatures and make predictions as to whether the signature in question is a forgery or not, has been successfully implemented. This model can be deployed at various government offices where handwritten signatures are used as a means of approval or authentication. While this method uses CNNs to learn the signatures,the structure of our fully connected layer is not optimal. This implementation may be considered extreme. In the model created in this work, two classes are created for each user (Real and forgery).The best accuracy we got was 95.5%.