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

Date

2022-01-01

Author

Ekiz, Ezgi
Şahin, Erol
Yarman Vural, Fatoş Tunay

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

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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.

Subject Keywords

fingerprinting, hypothesis testing problem, paper, Siamese Networks, texture

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138709078&origin=inward
https://hdl.handle.net/11511/101564

DOI

https://doi.org/10.1109/siu55565.2022.9864968

Conference Name

30th Signal Processing and Communications Applications Conference, SIU 2022

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

E. Ekiz, E. Şahin, and F. T. Yarman Vural, “Texture Analysis by Deep Twin Networks for Paper Fraud Detection Ikiz Derin Aǧlarla Doku Analizi ile Evrak Sahteciliǧinin Tesbiti,” presented at the 30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138709078&origin=inward.