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Identifying fraudulent identity documents by analyzing imprinted guilloche patterns

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Abstract

Identity document (ID) verification is crucial in fostering trust in the digital realm, especially with the increasing shift of transactions to online platforms. Our research, building upon our previous work (Al-Ghadi et al. 2023), delves deeper into ID verification by focusing on guilloche patterns. We present two innovative ID verification models leveraging contrastive and adversarial learning. These models enhance guilloche pattern detection, offering new insights into identifying counterfeit IDs. Each approach comprises two main components: (i) guilloche pattern recognition and feature generation using a convolutional neural network (CNN), and (ii) precise classification of input data as authentic or forged. We evaluate our models extensively on the MIDV and FMIDV datasets, achieving accuracy and F1-score results ranging from 68-92% and 75-100%, respectively. Our study, incorporating contrastive and adversarial learning, contributes significantly to the ongoing discourse on ID verification, specifically in analyzing guilloche patterns.

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Data Availability

Both the data and forgery detection code are made available at https://github.com/malghadi/CheckID.

Notes

  1. http://l3i-share.univ-lr.fr/2022FMIDV/2022FMIDV.html

  2. The dimensions of each latent vector are \(1664 \times 7 \times 7\).

  3. Here we have used the “Adaptive Average Pooling” algorithm from the PyTorch library. For more details, see: https://pytorch.org/cppdocs/api/classtorch_1_1nn_1_1_adaptive_avg_pool1d.html .

  4. The concatenated vector is of dimension \(3328 \times 7 \times 7\)

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Funding

This work has been financed by (le fonds unique interministériel) FUI IDECYS+ project (No.:DOS0098984/00)

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Authors and Affiliations

  1. L3i, University of La Rochelle, La Rochelle, France

    Musab Al-Ghadi, Petra Gomez-Krämer, Mickaël Coustaty, Nicolas Sidere & Jean-Christophe Burie

  2. IMT Atlantique, Brest, France

    Tanmoy Mondal

  3. L2TI, University Sorbonne Paris Nord, Paris, France

    Zuheng Ming

Authors
  1. Musab Al-Ghadi
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  2. Tanmoy Mondal
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  3. Zuheng Ming
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  4. Petra Gomez-Krämer
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  5. Mickaël Coustaty
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  6. Nicolas Sidere
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  7. Jean-Christophe Burie
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Corresponding author

Correspondence to Musab Al-Ghadi.

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The ID samples and the photos of individuals throughout this manuscript are sourced exclusively from the publicly available MIDV dataset.

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Al-Ghadi, M., Mondal, T., Ming, Z. et al. Identifying fraudulent identity documents by analyzing imprinted guilloche patterns. Multimed Tools Appl 83, 79145–79192 (2024). https://doi.org/10.1007/s11042-024-18611-3

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