An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach

Kao, Hsin-Hsiung; Wen, Che‐Yen

doi:10.3390/app10113716

Cited by 38 publications

(14 citation statements)

References 24 publications

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“…Their experiments with other CMFDs showed their scheme's enhanced detection and localization accuracies even under adverse image conditions. DCNNs (Deep Convolution Neural Networks) were exploited by Kao et al [99] for offline hand signature verifications. The study used novel local feature extractions using SigComp on ICDAR (Document Analysis and Recognitions) 2011 dataset.…”

Section: Review Of Classification and Deep Learning Methodsmentioning

confidence: 99%

A Thorough Investigation on Image Forgery Detection

Kumar¹,

Srivastava²

2023

Computer Modeling in Engineering &Amp; Sciences

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Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

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Section: Review Of Classification and Deep Learning Methodsmentioning

confidence: 99%

A Thorough Investigation on Image Forgery Detection

Kumar¹,

Srivastava²

2023

Computer Modeling in Engineering &Amp; Sciences

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“…In reference papers [1]- [7] various writer-dependent methodologies are studied which are based on deep learning. Convolution neural network shows a tremendous impact in the field of imagery.…”

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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“…It is evident that from this Table that the average performance of our proposed method is better than all the existing methods. The average performance based on accuracy, FAR and FRR are discussed in [ (29) [2020] Deep learning --94.37…”

Section: Performance Evaluationmentioning

confidence: 99%

Identification of Proper and Improper Signatures Using Graph Theory Techniques

Raj¹,

Venugopal²,

Padmapriya³

2022

IJST

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Objectives:To propose an automatic signature identification for off-line signature utilising graph theory approaches. Methods: Scanned signatures (Kaggle, https://www.kaggle.com/divyanshrai/handwritten-signatures/data) are collected for off-line signature data. The method follows pre-processing, vertex point extraction by midpoint traverse method, features extraction using edge, average edge and average edge D-distance and Support Vector Machine (SVM) to classify and predict the true label for the genuine and forged signatures. False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) give the accuracy of the proposed methods. This off-line signature verification method is compared with the deep learning techniques existing in the literature. Findings: Support Vector Machine (SVM) used for classification and results on standard signature datasets like ICDAR (International Conference on Document Analysis and Recognition). The results demonstrate how the proposed strategy outperforms the state-of-the-art already available. Novelty: The proposed approach use the edge distance, average edge distance, and average edge D-distance inbuilt graph structures to extract the feature points. Keywords: Signature images; grid approach; bipartite graph; complete bipartite graph; mid point traverse method Methodology Proposed methodThis section proposes an off-line signature automatic identification method that can be used to identify the signature's authenticity https://www.indjst.org/

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An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach

Cited by 38 publications

References 24 publications

A Thorough Investigation on Image Forgery Detection

A Thorough Investigation on Image Forgery Detection

Offline Handwritten Signature Recognition Using Deep Convolution Neural Network

Identification of Proper and Improper Signatures Using Graph Theory Techniques

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