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Towards minimizing efforts for Morphing Attacks-Deep embeddings for morphing pair selection and improved Morphing

Roman Kessler^1,2, Kiran Raja¹, Juan Tapia²

¹Norwegian University of Science and Technology (NTNU), Gjøvik, Norway.

|May 31, 2024

Summary

Face embeddings enhance the generation and detection of face morphing attacks. The MagFace model offers a robust alternative for detecting these attacks, improving security for identity documents.

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Area of Science:

Computer Science
Biometrics
Cybersecurity

Background:

Face morphing attacks threaten identity document security by enabling dual usage.
Existing detection methods often require extensive datasets of morphed images for training.
Face embeddings show promise for both generating and detecting morphing attacks.

Purpose of the Study:

To evaluate the efficacy of face embeddings, particularly the MagFace model, in generating and detecting face morphing attacks.
To analyze the vulnerability of face recognition systems to morphing attacks.
To compare the performance of different embedding models in attack detection.

Main Methods:

Developed an algorithm using face embeddings for pre-selecting images for large-scale morphing attack generation.
Utilized state-of-the-art face recognition systems (ArcFace, MagFace) to extract embeddings.
Assessed attack potential by quantifying the success rate of generated morphed images against various face recognition systems.
Compared the performance of ArcFace and MagFace embeddings in detecting morphed images.

Main Results:

Face embeddings significantly improve the pre-selection process for generating morphing attacks.
Many commercial and open-source face recognition systems are vulnerable, with vulnerability increasing when embeddings are used for pre-selection.
Landmark-based morphing algorithms and more accurate face recognition systems present higher risks.
MagFace embeddings demonstrate superior performance in detecting morphed images compared to ArcFace.

Conclusions:

Face embeddings are crucial for efficient generation of large-scale morphed face databases and for robust detection of morphing attacks.
The MagFace model is a powerful alternative for morphing attack detection, enhancing system security.
The study underscores the need for advanced countermeasures against sophisticated face morphing attacks.