Fraud detection from paper texture using Siamese networks

Emiroğlu E. E., ŞAHİN E., Vural F. T. Y.

Signal, Image and Video Processing, cilt.17, sa.7, ss.3369-3376, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 7
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11760-023-02558-3
  • Dergi Adı: Signal, Image and Video Processing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, zbMATH
  • Sayfa Sayıları: ss.3369-3376
  • Anahtar Kelimeler: Fraud detection, Hypothesis testing, Image matching, Siamese networks
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.