A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection

Nandanwar, Lokesh; Shivakumara, Palaiahnakote; A. Jalab, Hamid; W. Ibrahim, Rabha; Raghavendra, Ramachandra; Pal, Umapada; Lu, Tong; Blumenstein, Michael

Abstract

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.

Citation

Nandanwar, L., Shivakumara, P., A. Jalab, H., W. Ibrahim, R., Raghavendra, R., Pal, U., …Blumenstein, M. (2022). A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection. IEEE transactions on neural networks and learning systems, 1-14. https://doi.org/10.1109/TNNLS.2022.3204390

• Journal Article Type: Article
• Acceptance Date: Aug 22, 2022
• Publication Date: Sep 21, 2022
• Deposit Date: Feb 2, 2024
• Journal: IEEE Transactions on Neural Networks and Learning Systems
• Print ISSN: 2162-237X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1-14
• DOI: https://doi.org/10.1109/TNNLS.2022.3204390