Fraud detection from paper texture using Siamese networks

Date

2023-01-01

Author

Emiroğlu, Ezgi Ekiz
Şahin, Erol
Vural, Fatoş T. Yarman

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this paper, we present a model for the fraud detection of documents, using the texture of the paper on which they are printed. Different from prior studies, we present a data generation process through which we generate a dataset of papers and propose a deep learning model based on Siamese networks that is trained with samples from the dataset to reliably detect fraud from the original. Toward this end, we introduced a new regularization parameter for the training that would reduce the likelihood of the network making a Type-II error (i.e., classifying a fraud document as original), while being more tolerant of Type-I error (i.e., classifying an original document as fraud). Our analysis has shown that, combined with a Meta Learner, the proposed model can provide better fraud detection performance than that obtained with the Local Binary Pattern method, Prototypical Networks, and Matching Networks as the baseline.

Subject Keywords

Fraud detection, Hypothesis testing, Image matching, Siamese networks

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85152432322&origin=inward
https://hdl.handle.net/11511/102982

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Department of Computer Engineering, Article