Texture Analysis by Deep Twin Networks for Paper Fraud Detection Ikiz Derin Aǧlarla Doku Analizi ile Evrak Sahteciliǧinin Tesbiti

Ekiz E., ŞAHİN E., YARMAN VURAL F. T.

30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 15 - 18 Mayıs 2022 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu55565.2022.9864968
  • Basıldığı Şehir: Safranbolu
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: fingerprinting, hypothesis testing problem, paper, Siamese Networks, texture
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.This study proposes a method to distinguish fake documents from the originals using the textural structures of the papers they are printed on. The study is based on observations showing that paper textures are different and unique, just like fingerprint and iris tissue. This method, which captures the visually distinctive features of paper textures, can detect whether the documents of which the origin is suspected are fake or not. The proposed method can measure Type-2 error by training a Siamese network and thresholding the similarity results between two papers. Experimental results show that the proposed method has better distinguishing features than classical methods.