mlsanityData to 14 Oct 2026

Face Feature Visualisation of Single Morphing Attack Detection

arXiv 2304.13021 ↗First posted 25 Apr 2023cs.CVcited by 8

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Abstract (arXiv)

This paper proposes an explainable visualisation of different face feature extraction algorithms that enable the detection of bona fide and morphing images for single morphing attack detection. The feature extraction is based on raw image, shape, texture, frequency and compression. This visualisation may help to develop a Graphical User Interface for border policies and specifically for border guard personnel that have to investigate details of suspect images. A Random forest classifier was trained in a leave-one-out protocol on three landmarks-based face morphing methods and a StyleGAN-based morphing method for which morphed images are available in the FRLL database. For morphing attack detection, the Discrete Cosine-Transformation-based method obtained the best results for synthetic images and BSIF for landmark-based image features.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

6 of 27 references.

References · 6

  1. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  2. 2018Learning Rich Features for Image Manipulation Detectioncited by 154
  3. 2020Face Morphing Attack Generation & Detection: A Comprehensive Surveycited by 64
  4. 2020Morphing Attack Detection -- Database, Evaluation Platform and Benchmarkingcited by 38
  5. 2022Are GAN-based Morphs Threatening Face Recognition?cited by 28
  6. 2021Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Informationcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 2.

8 papers.

  1. 2025Empowering Morphing Attack Detection using Interpretable Image-Text Foundation Modelcited by 4
  2. 2024Generating Automatically Print/Scan Textures for Morphing Attack Detection Applicationscited by 2
  3. 2024Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detectioncited by 2
  4. 2026FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detectioncited by 2
  5. 2024On the Feasibility of Creating Iris Periocular Morphed Imagescited by 0
  6. 2025Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Featurescited by 0
  7. 2025Detection of Digital Facial Retouching utilizing Face Beauty Informationcited by 0
  8. 2026SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detectioncited by 0