Abstract
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.
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The raw dataset containing scanned paper textures and its augmented version publicly available along with the augmentation scripts is publicly available at https://github.com/ezgiekiz/fraud-detection-paper
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https://github.com/boyuanjiang/matching-networks-pytorch last access: 02.03.2023
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This study is partially supported by METU-ROMER (Center for Robotics and AI) infrastructure
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EEE conducted the experimental studies. FTYV and E.Ş. guided the work. All authors contributed to the writing of the manuscript text.
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Emiroğlu, E.E., Şahin, E. & Vural, F.T.Y. Fraud detection from paper texture using Siamese networks. SIViP 17, 3369–3376 (2023). https://doi.org/10.1007/s11760-023-02558-3
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DOI: https://doi.org/10.1007/s11760-023-02558-3