# MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregation

arXiv: https://arxiv.org/abs/2508.08939 · HTML: https://mlsanity.com/paper/2508.08939/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [Fixing the train-test resolution discrepancy](https://mlsanity.com/paper/1906.06423/) (2019)
- [Arc2Face: A Foundation Model for ID-Consistent Human Faces](https://mlsanity.com/paper/2403.11641/) (2024)
- [How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability](https://mlsanity.com/paper/2401.13641/) (2024)
- [MAAD-Face: A Massively Annotated Attribute Dataset for Face Images](https://mlsanity.com/paper/2012.01030/) (2020)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection](https://mlsanity.com/paper/2108.10291/) (2021)
- [FRoundation: Are Foundation Models Ready for Face Recognition?](https://mlsanity.com/paper/2410.23831/) (2024)
- [ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities](https://mlsanity.com/paper/2403.02965/) (2024)
- [ChatGPT Meets Iris Biometrics](https://mlsanity.com/paper/2408.04868/) (2024)
- [FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection](https://mlsanity.com/paper/2501.02892/) (2025)
- [MADation: Face Morphing Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.03800/) (2025)
- [Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study](https://mlsanity.com/paper/2304.03510/) (2023)

## Built on by (most cited first, 2 of 2)

- [DCMorph: Face Morphing via Dual-Stream Cross-Attention Diffusion](https://mlsanity.com/paper/2604.21627/) (2026)
- [On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection](https://mlsanity.com/paper/2604.20585/) (2026)

## Abstract (arXiv)

Face Morphing Attack Detection (MAD) is a critical challenge in face recognition security, where attackers can fool systems by interpolating the identity information of two or more individuals into a single face image, resulting in samples that can be verified as belonging to multiple identities by face recognition systems. While multimodal foundation models (FMs) like CLIP offer strong zero-shot capabilities by jointly modeling images and text, most prior works on FMs for biometric recognition have relied on fine-tuning for specific downstream tasks, neglecting their potential for direct, generalizable deployment. This work explores a pure zero-shot approach to MAD by leveraging CLIP without any additional training or fine-tuning, focusing instead on the design and aggregation of multiple textual prompts per class. By aggregating the embeddings of diverse prompts, we better align the model's internal representations with the MAD task, capturing richer and more varied cues indicative of bona-fide or attack samples. Our results show that prompt aggregation substantially improves zero-shot detection performance, demonstrating the effectiveness of exploiting foundation models' built-in multimodal knowledge through efficient prompt engineering.