Recurrent comparator with attention models to detect counterfeit documents

Albert Berenguel Centeno, Oriol Ramos Terrades, Josep Llados Canet, Cristina Canero Morales

Research output: Book/ReportProceedingResearchpeer-review

3 Citations (Scopus)

Abstract

This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation.

Original languageEnglish
Number of pages6
DOIs
Publication statusPublished - Sep 2019

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363

Keywords

  • Attention models
  • Background
  • Banknotes
  • Counterfeit
  • Glimpses
  • Identity documents
  • Recurrent comparator
  • Textures

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