# Empowering Morphing Attack Detection using Interpretable Image-Text Foundation Model

arXiv: https://arxiv.org/abs/2508.10110 · HTML: https://mlsanity.com/paper/2508.10110/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [FLIP: Cross-domain Face Anti-spoofing with Language Guidance](https://mlsanity.com/paper/2309.16649/) (2023)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer](https://mlsanity.com/paper/2501.09817/) (2025)
- [Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?](https://mlsanity.com/paper/2202.12426/) (2022)
- [Face Feature Visualisation of Single Morphing Attack Detection](https://mlsanity.com/paper/2304.13021/) (2023)
- [3D Face Morphing Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2201.03454/) (2022)
- [Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study](https://mlsanity.com/paper/2304.03510/) (2023)
- [Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniques](https://mlsanity.com/paper/2305.03264/) (2023)
- [Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network](https://mlsanity.com/paper/2305.01294/) (2023)
- [Vulnerability of Face Morphing Attacks: A Case Study on Lookalike and Identical Twins](https://mlsanity.com/paper/2303.14004/) (2023)

## Built on by (most cited first, 4 of 4)

- [LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet](https://mlsanity.com/paper/2511.12602/) (2025)
- [Emergent Morphing Attack Detection in Open Multi-modal Large Language Models](https://mlsanity.com/paper/2602.15461/) (2026)
- [R-FLoRA: Residual-Statistic-Gated Low-Rank Adaptation for Single-Image Face Morphing Attack Detection](https://mlsanity.com/paper/2604.17321/) (2026)
- [Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection](https://mlsanity.com/paper/2608.29802/) (2026)

## Abstract (arXiv)

Morphing attack detection has become an essential component of face recognition systems for ensuring a reliable verification scenario. In this paper, we present a multimodal learning approach that can provide a textual description of morphing attack detection. We first show that zero-shot evaluation of the proposed framework using Contrastive Language-Image Pretraining (CLIP) can yield not only generalizable morphing attack detection, but also predict the most relevant text snippet. We present an extensive analysis of ten different textual prompts that include both short and long textual prompts. These prompts are engineered by considering the human understandable textual snippet. Extensive experiments were performed on a face morphing dataset that was developed using a publicly available face biometric dataset. We present an evaluation of SOTA pre-trained neural networks together with the proposed framework in the zero-shot evaluation of five different morphing generation techniques that are captured in three different mediums.