# Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?

arXiv: https://arxiv.org/abs/2202.12426 · HTML: https://mlsanity.com/paper/2202.12426/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 60 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [Deep Face Representations for Differential Morphing Attack Detection](https://mlsanity.com/paper/2001.01202/) (2020)
- [Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking](https://mlsanity.com/paper/2006.06458/) (2020)
- [Face morphing detection in the presence of printing/scanning and heterogeneous image sources](https://mlsanity.com/paper/1901.08811/) (2019)
- [Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape and Diffuse Reflectance](https://mlsanity.com/paper/1912.01372/) (2019)

## Built on by (most cited first, 13 of 13)

- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [Deep Composite Face Image Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2211.11039/) (2022)
- [MorCode: Face Morphing Attack Generation using Generative Codebooks](https://mlsanity.com/paper/2410.07625/) (2024)
- [Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance](https://mlsanity.com/paper/2209.15474/) (2022)
- [Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study](https://mlsanity.com/paper/2304.03510/) (2023)
- [Empowering Morphing Attack Detection using Interpretable Image-Text Foundation Model](https://mlsanity.com/paper/2508.10110/) (2025)
- [Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network](https://mlsanity.com/paper/2305.01294/) (2023)
- [Vulnerability of Face Morphing Attacks: A Case Study on Lookalike and Identical Twins](https://mlsanity.com/paper/2303.14004/) (2023)
- [3D Face Morphing Attack Generation using Non-Rigid Registration](https://mlsanity.com/paper/2404.15765/) (2024)
- [FaceOracle: Chat with a Face Image Oracle](https://mlsanity.com/paper/2501.07202/) (2025)
- [Human-AI Ensembles Improve Deepfake Detection in Low-to-Medium Quality Videos](https://mlsanity.com/paper/2603.14658/) (2026)
- [WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation](https://mlsanity.com/paper/2507.12493/) (2025)
- [R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection](https://mlsanity.com/paper/2604.17321/) (2026)

## Abstract (arXiv)

Few studies have focused on examining how people recognize morphing attacks, even as several publications have examined the susceptibility of automated FRS and offered morphing attack detection (MAD) approaches. MAD approaches base their decisions either on a single image with no reference to compare against (S-MAD) or using a reference image (D-MAD). One prevalent misconception is that an examiner's or observer's capacity for facial morph detection depends on their subject expertise, experience, and familiarity with the issue and that no works have reported the specific results of observers who regularly verify identity (ID) documents for their jobs. As human observers are involved in checking the ID documents having facial images, a lapse in their competence can have significant societal challenges. To assess the observers' proficiency, this work first builds a new benchmark database of realistic morphing attacks from 48 different subjects, resulting in 400 morphed images. We also capture images from Automated Border Control (ABC) gates to mimic the realistic border-crossing scenarios in the D-MAD setting with 400 probe images to study the ability of human observers to detect morphed images. A new dataset of 180 morphing images is also produced to research human capacity in the S-MAD environment. In addition to creating a new evaluation platform to conduct S-MAD and D-MAD analysis, the study employs 469 observers for D-MAD and 410 observers for S-MAD who are primarily governmental employees from more than 40 countries, along with 103 subjects who are not examiners. The analysis offers intriguing insights and highlights the lack of expertise and failure to recognize a sizable number of morphing attacks by experts. The results of this study are intended to aid in the development of training programs to prevent security failures while determining whether an image is bona fide or altered.