FakeNets project:

morphological analysis for fake generation and similarity metrics for fake detection by neural networks
January 2022 - December 2023


The motivation of this project is to explore the potential offered by deep learning in the context of copy-sensitive graphical codes (such as Copy Detection Patterns) in secure printing, from the attacker's point of view and from the verifier's point of view.

Two main scientific challenges we address in the FakeNets project are:

Involved reserchers

Link via FIL projects

Publications related to the project

  1. H. Zeghidi, C. Crispim-Junior, I. Tkachenko, “CDP-Sim: Similarity metric learning to identify the fake Copy Detection Patterns”, IEEE WIFS 2023, December 2023, Nuremberg, Germany.[pdf]
  2. T. Yemelianenko, A. Trémeau, I. Tkachenko, “Printed packaging authentication: similarity metric learning for rotogravure manufacture process identification”, VISAPP 2023, February 2023, Lisbon, Portugal.[pdf][dataset]