mlsanityData to 14 Oct 2026

SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

arXiv 2208.07337 ↗First posted 15 Aug 2022cs.CVcited by 30

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Abstract (arXiv)

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people's privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 27 of its 58 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

27 of 58 references.

References · 27

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15 more
  1. 2020CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognitioncited by 321
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  5. 2018ArcFace: Additive Angular Margin Loss for Deep Face Recognitioncited by 56
  6. 2022Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectorscited by 47
  7. 2020Deep Face Representations for Differential Morphing Attack Detectioncited by 46
  8. 2020Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detectioncited by 46
  9. 2020Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networkscited by 36
  10. 2021MixFaceNets: Extremely Efficient Face Recognition Networkscited by 36
  11. 2021PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detectioncited by 29
  12. 2021About Face: A Survey of Facial Recognition Evaluationcited by 27
  13. 2021ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generationcited by 21
  14. 2021Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detectioncited by 10
  15. 2022Face Morphing Attack Detection Using Privacy-Aware Training Datacited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 6; 2025: 5; 2026: 8.

30 papers.

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  1. 2023MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoderscited by 44
  2. 2023Synthetic Data for Face Recognition: Current State and Future Prospectscited by 38
  3. 2023Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognitioncited by 27
  4. 2022Synthetic ID Card Image Generation for Improving Presentation Attack Detectioncited by 18
  5. 2023SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Datacited by 17
  6. 2025MADation: Face Morphing Attack Detection with Foundation Modelscited by 14
  7. 2023Face Morphing Attack Detection with Denoising Diffusion Probabilistic Modelscited by 12
  8. 2022OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglementcited by 10
  9. 2024Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphscited by 10
  10. 2022Causality-Inspired Taxonomy for Explainable Artificial Intelligencecited by 9
  11. 2023Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detectioncited by 9
  12. 2024AdjointDEIS: Efficient Gradients for Diffusion Modelscited by 7
18 more
  1. 2025SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learningcited by 7
  2. 2023Fast-DiM: Towards Fast Diffusion Morphscited by 6
  3. 2023SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Datacited by 5
  4. 2024Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domaincited by 3
  5. 2024SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samplescited by 3
  6. 2025MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregationcited by 2
  7. 2026FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detectioncited by 2
  8. 2024Impact of Synthetic Images on Morphing Attack Detection Using a Siamese Networkcited by 1
  9. 2025LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNetcited by 1
  10. 2026Emergent Morphing Attack Detection in Open Multi-modal Large Language Modelscited by 1
  11. 2026DCMorph: Face Morphing via Dual-Stream Cross-Attention Diffusioncited by 1
  12. 2024Hierarchical Generative Network for Face Morphing Attackscited by 0
  13. 2025Trade-offs in Cross-Domain Generalization of Foundation Model Fine-Tuned for Biometric Applicationscited by 0
  14. 2026SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detectioncited by 0
  15. 2026On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detectioncited by 0
  16. 2026Enhancing Single-Image Facial Demorphing using Multimodal Large Language Modelscited by 0
  17. 2026Face Re-morphing: Differential Morphing Attack Detection via Feature-Space Similarity Changescited by 0
  18. 2026XSA-MAD: Cross-modal Semantic Alignment for Morphing Attack Detectioncited by 0