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A Double Siamese Framework for Differential Morphing Attack Detection.

Guido Borghi1, Emanuele Pancisi1, Matteo Ferrara1

  • 1DISI-Dipartimento di Informatica-Scienza e Ingegneria, Università di Bologna, 47521 Cesena, Italy.

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|June 2, 2021
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Summary
This summary is machine-generated.

This study introduces a novel double Siamese architecture for detecting face morphing attacks. The framework effectively identifies morphed images by analyzing both facial identity and morphing artifacts, enhancing security for face recognition systems.

Keywords:
Siamese networksdeep learningdifferential morph attack detectionface morphingmorphing attack detectionsingle-image morph attack detection

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Area of Science:

  • Computer Science
  • Biometrics
  • Cybersecurity

Background:

  • Face morphing attacks pose a significant security risk to automated face recognition systems.
  • Developing reliable morphing attack detectors is crucial for maintaining system integrity.

Purpose of the Study:

  • To propose and evaluate a novel framework for detecting face morphing attacks in a differential scenario.
  • To enhance the security of face recognition systems against sophisticated morphing techniques.

Main Methods:

  • A double Siamese neural network architecture was developed.
  • The framework integrates two modules: one for facial identity extraction and another for morphing artifact detection.
  • The system analyzes a trusted live image and a probe image (morphed or bona fide).

Main Results:

  • The proposed framework demonstrated satisfying performance in rigorous cross-dataset tests.
  • Evaluations were conducted on the PMDB, MorphDB, and AMSL datasets, which include both automatic and manually refined morphed images.
  • The integrated approach of analyzing identity and artifacts proved effective.

Conclusions:

  • The double Siamese architecture offers a promising solution for reliable face morphing attack detection.
  • The framework's ability to merge information from identity and artifact modules enhances detection accuracy.
  • This research contributes to improving the robustness of face recognition systems against biometric security threats.