# Facial De-morphing: Extracting Component Faces from a Single Morph

arXiv: https://arxiv.org/abs/2209.02933 · HTML: https://mlsanity.com/paper/2209.02933/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking](https://mlsanity.com/paper/2006.06458/) (2020)
- [ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation](https://mlsanity.com/paper/2108.09130/) (2021)
- [Differential Morph Face Detection using Discriminative Wavelet Sub-bands](https://mlsanity.com/paper/2106.13178/) (2021)
- [Conditional Identity Disentanglement for Differential Face Morph Detection](https://mlsanity.com/paper/2107.02162/) (2021)
- [Morph Detection Enhanced by Structured Group Sparsity](https://mlsanity.com/paper/2111.14943/) (2021)
- [MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN](https://mlsanity.com/paper/2009.01729/) (2020)

## Built on by (most cited first, 9 of 9)

- [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023)
- [Facial Demorphing via Identity Preserving Image Decomposition](https://mlsanity.com/paper/2408.10993/) (2024)
- [SDeMorph: Towards Better Facial De-morphing from Single Morph](https://mlsanity.com/paper/2308.11442/) (2023)
- [dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph](https://mlsanity.com/paper/2411.14494/) (2024)
- [MorphGANFormer: Transformer-based Face Morphing and De-Morphing](https://mlsanity.com/paper/2302.09404/) (2023)
- [Metric for Evaluating Performance of Reference-Free Demorphing Methods](https://mlsanity.com/paper/2501.12319/) (2025)
- [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)
- [Enhancing Single-Image Facial Demorphing using Multimodal Large Language Models](https://mlsanity.com/paper/2605.25442/) (2026)

## Abstract (arXiv)

A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple identities. Current morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. The task of deducing the individual face images from a morphed face image is known as textitde-morphing. Existing work in de-morphing assume the availability of a reference image pertaining to one identity in order to recover the image of the accomplice - i.e., the other identity. In this work, we propose a novel de-morphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process. We propose a generative adversarial network that achieves single image-based de-morphing with a surprisingly high degree of visual realism and biometric similarity with the original face images. We demonstrate the performance of our method on landmark-based morphs and generative model-based morphs with promising results.