mlsanityData to 14 Oct 2026

OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement

arXiv 2208.07841 ↗First posted 16 Aug 2022cs.CVcited by 10

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Abstract (arXiv)

Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors. We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others. The code of this paper will be publicly available.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 16 Aug 2022.

Builds on

15 of 28 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  3. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  4. 2020Training Generative Adversarial Networks with Limited Datacited by 1,186
  5. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  6. 2021CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiabilitycited by 69
  7. 2022Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectorscited by 47
  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. 2022SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Datacited by 30
  12. 2021PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detectioncited by 29
3 more
  1. 2021FocusFace: Multi-task Contrastive Learning for Masked Face Recognitioncited by 12
  2. 2021Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Informationcited by 8
  3. 2021Myope Models -- Are face presentation attack detection models short-sighted?cited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 1; 2026: 4.

10 papers.

  1. 2022Causality-Inspired Taxonomy for Explainable Artificial Intelligencecited by 9
  2. 2023Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detectioncited by 9
  3. 2026FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detectioncited by 2
  4. 2024Quadruplet Loss For Improving the Robustness to Face Morphing Attackscited by 1
  5. 2023Towards Generalizable Morph Attack Detection with Consistency Regularizationcited by 0
  6. 2023Impact of Image Context for Single Deep Learning Face Morphing Attack Detectioncited by 0
  7. 2025MorphGuard: Morph Specific Margin Loss for Enhancing Robustness to Face Morphing Attackscited by 0
  8. 2026SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detectioncited by 0
  9. 2026Enhancing Single-Image Facial Demorphing using Multimodal Large Language Modelscited by 0
  10. 2026Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detectioncited by 0