Handwritten Signature Forgery Detection using Convolutional Neural Networks

Gideon, S Jerome; Kandulna, Anurag; Kujur, Aron Abhishek; Diana, Alessia; Raimond, Kumudha

doi:10.1016/j.procs.2018.10.336

Cited by 38 publications

(16 citation statements)

References 1 publication

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“…One limitation of the proposed work is: all experimentation was carried out without considering any forged signature data. Writer specific signature verification with forged signature data [32] need to explore as this task mirrors the situation of real world applications (especially for banking and legal sectors). Building such benchmark dataset for official Indic scripts is also an open area till date.…”

Section: Resultsmentioning

confidence: 99%

SigVer—A Deep Learning Based Writer Independent Bangla Signature Verification System

Obaidullah

Mukherjee

Pal

2021

Communications in Computer and Information Science

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Handwritten signature is one of the most popular biometric traits which have been addressed by researchers since the last few decades. Scientists have proposed disparate handcrafted feature-based techniques for signature verification in the past. Presently there has been an interest towards deep learning-based approaches which has demonstrated promising results across different avenues of pattern recognition problems. In this paper, light-weight CNN architecture named SigVer is proposed for Bangla offline writer independent signature verification. The proposed network was built with only six layers with no pooling in between convolution layers to minimize information loss. Experiments were performed on 2956 signatures from 57 writers and a highest accuracy of 99.49% was obtained. Further, comparative analysis was performed with established CNN architectures as well as handcrafted feature-based techniques and SigVer produced better results.

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Section: Resultsmentioning

confidence: 99%

SigVer—A Deep Learning Based Writer Independent Bangla Signature Verification System

Obaidullah

Mukherjee

Pal

2021

Communications in Computer and Information Science

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“…Images of handwritten signatures can be used for verification of printed documents. Gideon et al proposed a method using Convolutional Neural Networks (CNNs) to detect the probability of a handwritten signature being genuine or manipulated [178]. Beusekom et al analyzed text-line alignment in document images for their authentication [179].…”

Section: Text and Document Forensicsmentioning

confidence: 99%

An Overview of Recent Work in Media Forensics: Methods and Threats

Bhagtani¹,

Yadav²,

Bartusiak³

et al. 2022

Preprint

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In this paper, we review recent work in media forensics for digital images, video, audio (specifically speech), and documents. For each data modality, we discuss synthesis and manipulation techniques that can be used to create and modify digital media. We then review technological advancements for detecting and quantifying such manipulations. Finally, we consider open issues and suggest directions for future research.

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“…Writer-dependent data set is the opposite of it that is pair of (genuine, genuine), (genuine, forgery) was previously set in the data set. Inspired by Google Net an architecture called Inception SVG Net framework is used to implement CNN in [3].…”

Section: Related Workmentioning

confidence: 99%

Offline Handwritten Signature Recognition Using Deep Convolution Neural Network

Mosaher

Hasan²

2022

EJENG

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In the modern age, technological advancement reached a new limit where authentication plays a vital role in security management. Biometric-based authentication is the most referenced procedure for authentication where signature verification is a significant part of it for authentication of a person. To prevent the falsification of signatures on important documents & legal transactions it is necessary to recognize a person's signature accurately. This paper focused on recognizing offline handwritten original & forged signatures using a deep convolution neural network. We use a completely new dataset & also downloaded datasets to train the system & verify a random signature as genuine or forgery. All testing samples are collected from several individuals after several steps of preprocessing the model is fed with the resultant image to our system, the experimental results give us an accuracy of 95.5% from the dataset.

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Handwritten Signature Forgery Detection using Convolutional Neural Networks

Cited by 38 publications

References 1 publication

SigVer—A Deep Learning Based Writer Independent Bangla Signature Verification System

SigVer—A Deep Learning Based Writer Independent Bangla Signature Verification System

An Overview of Recent Work in Media Forensics: Methods and Threats

Offline Handwritten Signature Recognition Using Deep Convolution Neural Network

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