mlsanityData to 7 Oct 2026

PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection

arXiv 2108.10291 ↗First posted 23 Aug 2021cs.CVcited by 29

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Abstract (arXiv)

A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks. Various methods have been proposed to detect face morphing attacks, however, with low generalizability to unexpected post-morphing processes. A major post-morphing process is the print and scan operation performed in many countries when issuing a passport or identity document. In this work, we address this generalization problem by adapting a pixel-wise supervision approach where we train a network to classify each pixel of the image into an attack or not, rather than only having one label for the whole image. Our pixel-wise morphing attack detection (PW-MAD) solution proved to perform more accurately than a set of established baselines. More importantly, PW-MAD shows high generalizability in comparison to related works, when evaluated on unknown re-digitized attacks. Additionally to our PW-MAD approach, we create a new face morphing attack dataset with digital and re-digitized samples, namely the LMA-DRD dataset that is publicly available for research purposes upon request.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 14 of its 47 references that are in the map. First posted on arXiv 23 Aug 2021.

Builds on

14 of 47 references.

References · 14

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  5. 2019Deep Pixel-wise Binary Supervision for Face Presentation Attack Detectioncited by 76
  6. 2020Face Morphing Attack Generation & Detection: A Comprehensive Surveycited by 63
  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. 2019Face morphing detection in the presence of printing/scanning and heterogeneous image sourcescited by 22
  10. 2021ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generationcited by 21
  11. 2019Detection of Face Recognition Adversarial Attackscited by 15
  12. 2021Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Networkcited by 15
2 more
  1. 2020Differential Morphed Face Detection Using Deep Siamese Networkscited by 12
  2. 2021Detection of Morphed Face Images Using Discriminative Wavelet Sub-bandscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 6; 2024: 3; 2025: 3; 2026: 6.

29 papers.

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  1. 2022Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectorscited by 47
  2. 2023MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoderscited by 44
  3. 2022SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Datacited by 30
  4. 2024First Competition on Presentation Attack Detection on ID Cardcited by 18
  5. 2022Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detectioncited by 16
  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. 2023Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detectioncited by 9
  10. 2022Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Qualitycited by 8
  11. 2022Face Morphing Attack Detection Using Privacy-Aware Training Datacited by 7
  12. 2022MorDeephy: Face Morphing Detection Via Fused Classificationcited by 7
17 more
  1. 2022Robust Ensemble Morph Detection with Domain Generalizationcited by 7
  2. 2025SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learningcited by 7
  3. 2024IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detectioncited by 6
  4. 2021Algorithmic Fairness in Face Morphing Attack Detectioncited by 5
  5. 2023Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniquescited by 3
  6. 2022Single Morphing Attack Detection using Siamese Network and Few-shot Learningcited by 2
  7. 2024Generating Automatically Print/Scan Textures for Morphing Attack Detection Applicationscited by 2
  8. 2025MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregationcited by 2
  9. 2026FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detectioncited by 2
  10. 2022Fusion-based Few-Shot Morphing Attack Detection and Fingerprintingcited by 1
  11. 2023Fused Classification For Differential Face Morphing Detectioncited by 1
  12. 2026Emergent Morphing Attack Detection in Open Multi-modal Large Language Modelscited by 1
  13. 2023Towards Generalizable Morph Attack Detection with Consistency Regularizationcited by 0
  14. 2026SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detectioncited by 0
  15. 2026R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detectioncited by 0
  16. 2026Enhancing Single-Image Facial Demorphing using Multimodal Large Language Modelscited by 0
  17. 2026XSA-MAD: Cross-modal Semantic Alignment for Morphing Attack Detectioncited by 0