Staff View: A conformable moments-based deep learning system for forged handwriting detection

A conformable moments-based deep learning system for forged handwriting detection

Detecting forged handwriting is important in a wide variety of machine learning applications, and it is challenging when the input images are degraded with noise and blur. This article presents a new model based on conformable moments (CMs) and deep ensemble neural networks (DENNs) for forged handwr...

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Main Authors:	Nandanwar, Lokesh, Shivakumara, Palaiahnakote, Jalab, Hamid A., Ibrahim, Rabha W., Raghavendra, Ramachandra, Pal, Umapada, Lu, Tong, Blumenstein, Michael
Format:	Article
Published:	Institute of Electrical and Electronics Engineers (IEEE) 2024
Subjects:	QA Mathematics QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/46097/
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spelling	my.um.eprints.460972024-08-13T02:52:23Z http://eprints.um.edu.my/46097/ A conformable moments-based deep learning system for forged handwriting detection Nandanwar, Lokesh Shivakumara, Palaiahnakote Jalab, Hamid A. Ibrahim, Rabha W. Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael QA Mathematics QA75 Electronic computers. Computer science Detecting forged handwriting is important in a wide variety of machine learning applications, and it is challenging when the input images are degraded with noise and blur. This article presents a new model based on conformable moments (CMs) and deep ensemble neural networks (DENNs) for forged handwriting detection in noisy and blurry environments. Since CMs involve fractional calculus with the ability to model nonlinearities and geometrical moments as well as preserving spatial relationships between pixels, fine details in images are preserved. This motivates us to introduce a DENN classifier, which integrates stenographic kernels and spatial features to classify input images as normal (original, clean images), altered (handwriting changed through copy-paste and insertion operations), noisy (added noise to original image), blurred (added blur to original image), altered-noise (noise is added to the altered image), and altered-blurred (blur is added to the altered image). To evaluate our model, we use a newly introduced dataset, which comprises handwritten words altered at the character level, as well as several standard datasets, namely ACPR 2019, ICPR 2018-FDC, and the IMEI dataset. The first two of these datasets include handwriting samples that are altered at the character and word levels, and the third dataset comprises forged International Mobile Equipment Identity (IMEI) numbers. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of classification rate. Institute of Electrical and Electronics Engineers (IEEE) 2024-04 Article PeerReviewed Nandanwar, Lokesh and Shivakumara, Palaiahnakote and Jalab, Hamid A. and Ibrahim, Rabha W. and Raghavendra, Ramachandra and Pal, Umapada and Lu, Tong and Blumenstein, Michael (2024) A conformable moments-based deep learning system for forged handwriting detection. IEEE Transactions on Neural Networks and Learning Systems, 35 (4). pp. 5407-5420. ISSN 2162-237X, DOI https://doi.org/10.1109/TNNLS.2022.3204390 <https://doi.org/10.1109/TNNLS.2022.3204390>. 10.1109/TNNLS.2022.3204390
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA Mathematics QA75 Electronic computers. Computer science
spellingShingle	QA Mathematics QA75 Electronic computers. Computer science Nandanwar, Lokesh Shivakumara, Palaiahnakote Jalab, Hamid A. Ibrahim, Rabha W. Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael A conformable moments-based deep learning system for forged handwriting detection
description	Detecting forged handwriting is important in a wide variety of machine learning applications, and it is challenging when the input images are degraded with noise and blur. This article presents a new model based on conformable moments (CMs) and deep ensemble neural networks (DENNs) for forged handwriting detection in noisy and blurry environments. Since CMs involve fractional calculus with the ability to model nonlinearities and geometrical moments as well as preserving spatial relationships between pixels, fine details in images are preserved. This motivates us to introduce a DENN classifier, which integrates stenographic kernels and spatial features to classify input images as normal (original, clean images), altered (handwriting changed through copy-paste and insertion operations), noisy (added noise to original image), blurred (added blur to original image), altered-noise (noise is added to the altered image), and altered-blurred (blur is added to the altered image). To evaluate our model, we use a newly introduced dataset, which comprises handwritten words altered at the character level, as well as several standard datasets, namely ACPR 2019, ICPR 2018-FDC, and the IMEI dataset. The first two of these datasets include handwriting samples that are altered at the character and word levels, and the third dataset comprises forged International Mobile Equipment Identity (IMEI) numbers. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of classification rate.
format	Article
author	Nandanwar, Lokesh Shivakumara, Palaiahnakote Jalab, Hamid A. Ibrahim, Rabha W. Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael
author_facet	Nandanwar, Lokesh Shivakumara, Palaiahnakote Jalab, Hamid A. Ibrahim, Rabha W. Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael
author_sort	Nandanwar, Lokesh
title	A conformable moments-based deep learning system for forged handwriting detection
title_short	A conformable moments-based deep learning system for forged handwriting detection
title_full	A conformable moments-based deep learning system for forged handwriting detection
title_fullStr	A conformable moments-based deep learning system for forged handwriting detection
title_full_unstemmed	A conformable moments-based deep learning system for forged handwriting detection
title_sort	conformable moments-based deep learning system for forged handwriting detection
publisher	Institute of Electrical and Electronics Engineers (IEEE)
publishDate	2024
url	http://eprints.um.edu.my/46097/
_version_	1809136926953832448
score	13.211869

A conformable moments-based deep learning system for forged handwriting detection

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