# OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement

arXiv: https://arxiv.org/abs/2208.07841 · HTML: https://mlsanity.com/paper/2208.07841/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability](https://mlsanity.com/paper/2112.06592/) (2021)
- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection](https://mlsanity.com/paper/2007.03621/) (2020)
- [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)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection](https://mlsanity.com/paper/2108.10291/) (2021)
- [FocusFace: Multi-task Contrastive Learning for Masked Face Recognition](https://mlsanity.com/paper/2110.14940/) (2021)
- [Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Information](https://mlsanity.com/paper/2110.13552/) (2021)
- [Myope Models -- Are face presentation attack detection models short-sighted?](https://mlsanity.com/paper/2111.11127/) (2021)

## Built on by (most cited first, 10 of 10)

- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023)
- [FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detection](https://mlsanity.com/paper/2601.20656/) (2026)
- [Quadruplet Loss For Improving the Robustness to Face Morphing Attacks](https://mlsanity.com/paper/2402.14665/) (2024)
- [Towards Generalizable Morph Attack Detection with Consistency Regularization](https://mlsanity.com/paper/2308.10392/) (2023)
- [Impact of Image Context for Single Deep Learning Face Morphing Attack Detection](https://mlsanity.com/paper/2309.00549/) (2023)
- [MorphGuard: Morph Specific Margin Loss for Enhancing Robustness to Face Morphing Attacks](https://mlsanity.com/paper/2505.10497/) (2025)
- [SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detection](https://mlsanity.com/paper/2603.15050/) (2026)
- [Enhancing Single-Image Facial Demorphing using Multimodal Large Language Models](https://mlsanity.com/paper/2605.25442/) (2026)
- [Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection](https://mlsanity.com/paper/2608.29802/) (2026)

## Abstract (arXiv)

Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors. We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others. The code of this paper will be publicly available.