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)


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
Publication statusPublished - Sep 2019

Publication series

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


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

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