# MorCode: Face Morphing Attack Generation using Generative Codebooks

arXiv: https://arxiv.org/abs/2410.07625 · HTML: https://mlsanity.com/paper/2410.07625/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 10 Oct 2024.

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

- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection](https://mlsanity.com/paper/2007.03621/) (2020)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation](https://mlsanity.com/paper/2108.09130/) (2021)
- [Leveraging Diffusion For Strong and High Quality Face Morphing Attacks](https://mlsanity.com/paper/2301.04218/) (2023)
- [Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?](https://mlsanity.com/paper/2202.12426/) (2022)
- [Deep Composite Face Image Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2211.11039/) (2022)
- [3D Face Morphing Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2201.03454/) (2022)
- [ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping](https://mlsanity.com/paper/2310.12736/) (2023)

## Built on by (most cited first, 6 of 6)

- [SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning](https://mlsanity.com/paper/2504.05504/) (2025)
- [DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing](https://mlsanity.com/paper/2507.17158/) (2025)
- [LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet](https://mlsanity.com/paper/2511.12602/) (2025)
- [DCMorph: Face Morphing via Dual-Stream Cross-Attention Diffusion](https://mlsanity.com/paper/2604.21627/) (2026)
- [MorphGuard: Morph Specific Margin Loss for Enhancing Robustness to Face Morphing Attacks](https://mlsanity.com/paper/2505.10497/) (2025)
- [Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face](https://mlsanity.com/paper/2602.16569/) (2026)

## Abstract (arXiv)

Face recognition systems (FRS) can be compromised by face morphing attacks, which blend textural and geometric information from multiple facial images. The rapid evolution of generative AI, especially Generative Adversarial Networks (GAN) or Diffusion models, where encoded images are interpolated to generate high-quality face morphing images. In this work, we present a novel method for the automatic face morphing generation method textitMorCode, which leverages a contemporary encoder-decoder architecture conditioned on codebook learning to generate high-quality morphing images. Extensive experiments were performed on the newly constructed morphing dataset using five state-of-the-art morphing generation techniques using both digital and print-scan data. The attack potential of the proposed morphing generation technique, textitMorCode, was benchmarked using three different face recognition systems. The obtained results indicate the highest attack potential of the proposed textitMorCode when compared with five state-of-the-art morphing generation methods on both digital and print scan data.