# Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study

arXiv: https://arxiv.org/abs/2304.03510 · HTML: https://mlsanity.com/paper/2304.03510/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [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)
- [Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape and Diffuse Reflectance](https://mlsanity.com/paper/1912.01372/) (2019)
- [Mutual Information Maximization on Disentangled Representations for Differential Morph Detection](https://mlsanity.com/paper/2012.01542/) (2020)
- [Attention Aware Wavelet-based Detection of Morphed Face Images](https://mlsanity.com/paper/2106.15686/) (2021)
- [Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?](https://mlsanity.com/paper/2202.12426/) (2022)
- [Differential Morphed Face Detection Using Deep Siamese Networks](https://mlsanity.com/paper/2012.01541/) (2020)
- [Conditional Identity Disentanglement for Differential Face Morph Detection](https://mlsanity.com/paper/2107.02162/) (2021)
- [MorDeephy: Face Morphing Detection Via Fused Classification](https://mlsanity.com/paper/2208.03110/) (2022)
- [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)

## Built on by (most cited first, 5 of 5)

- [Empowering Morphing Attack Detection using Interpretable Image-Text Foundation Model](https://mlsanity.com/paper/2508.10110/) (2025)
- [MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregation](https://mlsanity.com/paper/2508.08939/) (2025)
- [WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation](https://mlsanity.com/paper/2507.12493/) (2025)
- [In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems](https://mlsanity.com/paper/2507.15285/) (2025)
- [DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection](https://mlsanity.com/paper/2604.17961/) (2026)

## Abstract (arXiv)

Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multispectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images.