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

TL;DR

This paper presents MADPromptS, a zero-shot face morphing attack detection method that leverages multiple prompt aggregation with CLIP, eliminating the need for fine-tuning and significantly improving detection accuracy.

Contribution

It introduces a novel prompt aggregation technique for CLIP that enhances zero-shot face morphing attack detection without additional training.

Findings

Prompt aggregation improves detection performance

Zero-shot approach matches or exceeds fine-tuned models

Efficient prompt engineering captures richer cues

Abstract

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.