# Mutual Information Maximization on Disentangled Representations for Differential Morph Detection

arXiv: https://arxiv.org/abs/2012.01542 · HTML: https://mlsanity.com/paper/2012.01542/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [Multimodal Variational Auto-encoder based Audio-Visual Segmentation](https://mlsanity.com/paper/2310.08303/) (2023)
- [Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection](https://mlsanity.com/paper/2208.05787/) (2022)
- [Attention Aware Wavelet-based Detection of Morphed Face Images](https://mlsanity.com/paper/2106.15686/) (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)
- [Detection of Morphed Face Images Using Discriminative Wavelet Sub-bands](https://mlsanity.com/paper/2106.08565/) (2021)
- [Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection](https://mlsanity.com/paper/2306.03630/) (2023)
- [Robust Ensemble Morph Detection with Domain Generalization](https://mlsanity.com/paper/2209.08130/) (2022)
- [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)
- [Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study](https://mlsanity.com/paper/2304.03510/) (2023)
- [Landmark Enforcement and Style Manipulation for Generative Morphing](https://mlsanity.com/paper/2210.10182/) (2022)
- [Towards Generalizable Morph Attack Detection with Consistency Regularization](https://mlsanity.com/paper/2308.10392/) (2023)
- [Face Re-morphing: Differential Morphing Attack Detection via Feature-Space Similarity Changes](https://mlsanity.com/paper/2608.13858/) (2026)

## Abstract (arXiv)

In this paper, we present a novel differential morph detection framework, utilizing landmark and appearance disentanglement. In our framework, the face image is represented in the embedding domain using two disentangled but complementary representations. The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image. This initially trained network is further trained for each dataset using contrastive representations. We demonstrate that, by employing appearance and landmark disentanglement, the proposed framework can provide state-of-the-art differential morph detection performance. This functionality is achieved by the using distances in landmark, appearance, and ID domains. The performance of the proposed framework is evaluated using three morph datasets generated with different methodologies.