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Resources for Morphing Attack

Code, data, and model collections publicly available.

Note

Cite the original paper if you use the implementation on this page!

Important

It is forbidden to use the following S-MAD and D-MAD models on FVC and NIST benchmarks without the consent of the authors.

D-MAD models

  • A double siamese framework for differential morphing attack detection (Borghi et al., Sensors 2021)

    • Original paper
    • Resource
    • Code: Python, PyTorch
    • Training datasets: PMDB, MorphDB, Idiap-morph datasets
    • Notes: official implementation, tested on FVC (SOTAMD_D-1.0 EER = 23.37%)
  • Combining identity features and artifact analysis for Differential Morphing Attack Detection (Di Domenico et al., ICIAP 2023)

    • Original paper
    • Resource
    • Training datasets: PMDB, MorphDB, Idiap-morph
    • Notes: official implementation, tested on FVC (SOTAMD_D-1.0 EER = 10.23%)
  • Dealing with Subject Similarity in Differential Morphing Attack Detection (Di Domenico et al., under submission)

    • The paper is currently under submission
    • Resource
    • Training datasets: PMDB, MorphDB, Idiap-morph
    • Notes: official implementation, tested on FVC (SOTAMD_D-1.0 EER = 7.84%)
  • Deep Face Representations for Differential Morphing Attack Detection (Scherag et al., TIFS 2020)

    • Original paper
    • Resource
    • Code: Python, PyTorch, MXNet
    • Training datasets: PMDB
    • Notes: unofficial implementation, tested on FVC (MORPHDB_D-1.0 EER = 0.0%)

S-MAD models

  • UBO-SMAD-R3: an Inception-ResNet-based model for Single-image Morphing Attack Detection
    • Resource
    • Code: Python, PyTorch
    • Training datasets: PMDB, MorphDB, Idiap-morph, Chimo
    • Notes: tested on FVC-onGoing (SOTAMD_D-1.0 EER = 10.33%)

Frameworks, Scripts and other resources

  • Revelio framework
  • FVC-onGoing platform
    • Link
    • S-MAD and D-MAD evaluations on sequestered datasets.
  • FRVT MORPH
    • Link
    • S-MAD and D-MAD evaluations on sequestered datasets.

Datasets

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