# Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2012.05344 · HTML: https://mlsanity.com/paper/2012.05344/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 30 of 36)

- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection](https://mlsanity.com/paper/2208.05787/) (2022)
- [Leveraging Diffusion For Strong and High Quality Face Morphing Attacks](https://mlsanity.com/paper/2301.04218/) (2023)
- [MADation: Face Morphing Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.03800/) (2025)
- [Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.15733/) (2023)
- [OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement](https://mlsanity.com/paper/2208.07841/) (2022)
- [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023)
- [Neural Implicit Morphing of Face Images](https://mlsanity.com/paper/2308.13888/) (2023)
- [Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality](https://mlsanity.com/paper/2208.05864/) (2022)
- [Face Morphing Attack Detection Using Privacy-Aware Training Data](https://mlsanity.com/paper/2207.00899/) (2022)
- [MorDeephy: Face Morphing Detection Via Fused Classification](https://mlsanity.com/paper/2208.03110/) (2022)
- [Robust Ensemble Morph Detection with Domain Generalization](https://mlsanity.com/paper/2209.08130/) (2022)
- [SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning](https://mlsanity.com/paper/2504.05504/) (2025)
- [Deep Composite Face Image Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2211.11039/) (2022)
- [IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection](https://mlsanity.com/paper/2408.01690/) (2024)
- [Adversarially Perturbed Wavelet-based Morphed Face Generation](https://mlsanity.com/paper/2111.01965/) (2021)
- [SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples](https://mlsanity.com/paper/2409.05595/) (2024)
- [FLOWING: Implicit Neural Flows for Structure-Preserving Morphing](https://mlsanity.com/paper/2510.09537/) (2025)
- [Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN](https://mlsanity.com/paper/2310.08371/) (2023)
- [Metric for Evaluating Performance of Reference-Free Demorphing Methods](https://mlsanity.com/paper/2501.12319/) (2025)
- [FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detection](https://mlsanity.com/paper/2601.20656/) (2026)
- [Landmark Enforcement and Style Manipulation for Generative Morphing](https://mlsanity.com/paper/2210.10182/) (2022)
- [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)
- [Optimal-Landmark-Guided Image Blending for Face Morphing Attacks](https://mlsanity.com/paper/2401.16722/) (2024)
- [diffDemorph: Extending Reference-Free Demorphing to Unseen Faces](https://mlsanity.com/paper/2505.14527/) (2025)
- [Facial Demorphing from a Single Morph Using a Latent Conditional GAN](https://mlsanity.com/paper/2507.18566/) (2025)
- [Towards Generalizable Morph Attack Detection with Consistency Regularization](https://mlsanity.com/paper/2308.10392/) (2023)

## Abstract (arXiv)

Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in face morphing attack detection is developing rapidly, however very few datasets with several forms of attacks are publicly available. This paper bridges this gap by providing a new dataset with four different types of morphing attacks, based on OpenCV, FaceMorpher, WebMorph and a generative adversarial network (StyleGAN), generated with original face images from three public face datasets. We also conduct extensive experiments to assess the vulnerability of the state-of-the-art face recognition systems, notably FaceNet, VGG-Face, and ArcFace. The experiments demonstrate that VGG-Face, while being less accurate face recognition system compared to FaceNet, is also less vulnerable to morphing attacks. Also, we observed that na"ive morphs generated with a StyleGAN do not pose a significant threat.