mlsanityData to 7 Oct 2026

SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples

arXiv 2409.05595 ↗First posted 9 Sep 2024cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

Face morphing attack detection (MAD) algorithms have become essential to overcome the vulnerability of face recognition systems. To solve the lack of large-scale and public-available datasets due to privacy concerns and restrictions, in this work we propose a new method to generate a synthetic face morphing dataset with 2450 identities and more than 100k morphs. The proposed synthetic face morphing dataset is unique for its high-quality samples, different types of morphing algorithms, and the generalization for both single and differential morphing attack detection algorithms. For experiments, we apply face image quality assessment and vulnerability analysis to evaluate the proposed synthetic face morphing dataset from the perspective of biometric sample quality and morphing attack potential on face recognition systems. The results are benchmarked with an existing SOTA synthetic dataset and a representative non-synthetic and indicate improvement compared with the SOTA. Additionally, we design different protocols and study the applicability of using the proposed synthetic dataset on training morphing attack detection algorithms.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 59 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

35 of 59 references.

References · 35

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23 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025ChatGPT Encounters Morphing Attack Detection: Zero-Shot MAD with Multi-Modal Large Language Models and General Vision Modelscited by 0
  2. 2026DifFoundMAD: Foundation Models meet Differential Morphing Attack Detectioncited by 0
  3. 2026XSA-MAD: Cross-modal Semantic Alignment for Morphing Attack Detectioncited by 0