# Face Morphing Attack Detection Using Privacy-Aware Training Data

arXiv: https://arxiv.org/abs/2207.00899 · HTML: https://mlsanity.com/paper/2207.00899/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 2 Jul 2022.

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

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks](https://mlsanity.com/paper/2012.05344/) (2020)
- [MixFaceNets: Extremely Efficient Face Recognition Networks](https://mlsanity.com/paper/2107.13046/) (2021)
- [PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection](https://mlsanity.com/paper/2108.10291/) (2021)
- [Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations](https://mlsanity.com/paper/1710.01494/) (2017)
- [Reflection Analysis for Face Morphing Attack Detection](https://mlsanity.com/paper/1807.02030/) (2018)

## Built on by (most cited first, 7 of 7)

- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [MADation: Face Morphing Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.03800/) (2025)
- [Beyond Detection: Visual Realism Assessment of Deepfakes](https://mlsanity.com/paper/2306.05985/) (2023)
- [Fair GANs through model rebalancing for extremely imbalanced class distributions](https://mlsanity.com/paper/2308.08638/) (2023)
- [Towards Generalizable Morph Attack Detection with Consistency Regularization](https://mlsanity.com/paper/2308.10392/) (2023)
- [Hierarchical Generative Network for Face Morphing Attacks](https://mlsanity.com/paper/2403.11101/) (2024)
- [XSA-MAD: Cross-modal Semantic Alignment for Morphing Attack Detection](https://mlsanity.com/paper/2608.13861/) (2026)

## Abstract (arXiv)

Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios.