# Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking

arXiv: https://arxiv.org/abs/2006.06458 · HTML: https://mlsanity.com/paper/2006.06458/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 30 of 38)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Morphing Attack Potential](https://mlsanity.com/paper/2204.13374/) (2022)
- [Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer](https://mlsanity.com/paper/2501.09817/) (2025)
- [Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?](https://mlsanity.com/paper/2202.12426/) (2022)
- [Conditional Identity Disentanglement for Differential Face Morph Detection](https://mlsanity.com/paper/2107.02162/) (2021)
- [Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs](https://mlsanity.com/paper/2404.06025/) (2024)
- [ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset](https://mlsanity.com/paper/2404.11236/) (2024)
- [Facial De-morphing: Extracting Component Faces from a Single Morph](https://mlsanity.com/paper/2209.02933/) (2022)
- [Neural Implicit Morphing of Face Images](https://mlsanity.com/paper/2308.13888/) (2023)
- [Face Feature Visualisation of Single Morphing Attack Detection](https://mlsanity.com/paper/2304.13021/) (2023)
- [MorDeephy: Face Morphing Detection Via Fused Classification](https://mlsanity.com/paper/2208.03110/) (2022)
- [SDeMorph: Towards Better Facial De-morphing from Single Morph](https://mlsanity.com/paper/2308.11442/) (2023)
- [Algorithmic Fairness in Face Morphing Attack Detection](https://mlsanity.com/paper/2111.12115/) (2021)
- [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)
- [MorphGANFormer: Transformer-based Face Morphing and De-Morphing](https://mlsanity.com/paper/2302.09404/) (2023)
- [Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection](https://mlsanity.com/paper/2305.18216/) (2023)
- [Worst-Case Morphs: a Theoretical and a Practical Approach](https://mlsanity.com/paper/2111.15416/) (2021)
- [Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniques](https://mlsanity.com/paper/2305.03264/) (2023)
- [SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples](https://mlsanity.com/paper/2409.05595/) (2024)
- [Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching](https://mlsanity.com/paper/2103.03654/) (2021)
- [Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN](https://mlsanity.com/paper/2310.08371/) (2023)
- [TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks](https://mlsanity.com/paper/2401.11598/) (2024)
- [The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios](https://mlsanity.com/paper/2404.06559/) (2024)
- [Dealing with Subject Similarity in Differential Morphing Attack Detection](https://mlsanity.com/paper/2404.07667/) (2024)
- [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)
- [Metric for Evaluating Performance of Reference-Free Demorphing Methods](https://mlsanity.com/paper/2501.12319/) (2025)
- [Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting](https://mlsanity.com/paper/2210.15510/) (2022)
- [Detecting Morphing Attacks via Continual Incremental Training](https://mlsanity.com/paper/2307.15105/) (2023)
- [Fused Classification For Differential Face Morphing Detection](https://mlsanity.com/paper/2309.00665/) (2023)

## Abstract (arXiv)

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.