# Face Re-morphing: Differential Morphing Attack Detection via Feature-Space Similarity Changes

arXiv: https://arxiv.org/abs/2608.13858 · HTML: https://mlsanity.com/paper/2608.13858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Deep Face Representations for Differential Morphing Attack Detection](https://mlsanity.com/paper/2001.01202/) (2020)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking](https://mlsanity.com/paper/2006.06458/) (2020)
- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks](https://mlsanity.com/paper/2012.05344/) (2020)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [Are GAN-based Morphs Threatening Face Recognition?](https://mlsanity.com/paper/2205.02496/) (2022)
- [Mutual Information Maximization on Disentangled Representations for Differential Morph Detection](https://mlsanity.com/paper/2012.01542/) (2020)
- [Dealing with Subject Similarity in Differential Morphing Attack Detection](https://mlsanity.com/paper/2404.07667/) (2024)

## Abstract (arXiv)

Face morphing attacks pose a serious threat to face recognition systems because a single morphed document image can be matched to multiple contributors. Differential morphing attack detection (D-MAD) addresses this threat by comparing a document image with a trusted live image, but existing methods often rely on static feature differences, constituent-face reconstruction, or multi-cue fusion. This paper proposes Face Re-morphing, a D-MAD method that uses the feature-space response to an additional morphing operation as a detection cue. Given a document image and a trusted live image, the proposed method generates a re-morphed image and uses the change between the document--live and live--re-morphed cosine similarities as the detection score. Experiments on FRLL-Morphs and FEI Morph show that the proposed cue is effective across different morphing conditions, re-morphing methods, and face recognition models. Comparisons with existing methods show favorable results on AMSL and indicate that the proposed method performs well under the Criminal condition on FEI Morph Version~1, particularly when using MorDIFF. These results indicate that re-morphing-induced similarity change provides a complementary cue for D-MAD.