# Face Feature Visualisation of Single Morphing Attack Detection

arXiv: https://arxiv.org/abs/2304.13021 · HTML: https://mlsanity.com/paper/2304.13021/

## 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 (most cited first, 6 of 6)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking](https://mlsanity.com/paper/2006.06458/) (2020)
- [Are GAN-based Morphs Threatening Face Recognition?](https://mlsanity.com/paper/2205.02496/) (2022)
- [Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Information](https://mlsanity.com/paper/2110.13552/) (2021)

## Built on by (most cited first, 8 of 8)

- [Empowering Morphing Attack Detection using Interpretable Image-Text Foundation Model](https://mlsanity.com/paper/2508.10110/) (2025)
- [Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications](https://mlsanity.com/paper/2408.09558/) (2024)
- [Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection](https://mlsanity.com/paper/2410.16802/) (2024)
- [FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detection](https://mlsanity.com/paper/2601.20656/) (2026)
- [On the Feasibility of Creating Iris Periocular Morphed Images](https://mlsanity.com/paper/2408.13496/) (2024)
- [Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features](https://mlsanity.com/paper/2507.20608/) (2025)
- [Detection of Digital Facial Retouching utilizing Face Beauty Information](https://mlsanity.com/paper/2512.08397/) (2025)
- [SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detection](https://mlsanity.com/paper/2603.15050/) (2026)

## 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.