Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning

TL;DR

This paper investigates GPT-4o's effectiveness in face presentation attack detection, showing its strengths in few-shot learning and interpretability, while highlighting challenges in zero-shot scenarios compared to specialized models.

Contribution

It demonstrates GPT-4o's potential as a competitive face PAD tool, emphasizing its emergent reasoning and interpretability capabilities in few-shot settings.

Findings

GPT-4o outperforms some commercial PAD models in specific scenarios.

Few-shot in-context learning improves GPT-4o's performance with more examples.

Detailed prompts and explanation-seeking prompts enhance model reliability and interpretability.

Abstract

This study highlights the potential of ChatGPT (specifically GPT-4o) as a competitive alternative for Face Presentation Attack Detection (PAD), outperforming several PAD models, including commercial solutions, in specific scenarios. Our results show that GPT-4o demonstrates high consistency, particularly in few-shot in-context learning, where its performance improves as more examples are provided (reference data). We also observe that detailed prompts enable the model to provide scores reliably, a behavior not observed with concise prompts. Additionally, explanation-seeking prompts slightly enhance the model's performance by improving its interpretability. Remarkably, the model exhibits emergent reasoning capabilities, correctly predicting the attack type (print or replay) with high accuracy in few-shot scenarios, despite not being explicitly instructed to classify attack types. Despite these strengths, GPT-4o faces challenges in zero-shot tasks, where its performance is limited compared to specialized PAD systems. Experiments were conducted on a subset of the SOTERIA dataset, ensuring compliance with data privacy regulations by using only data from consenting individuals. These findings underscore GPT-4o's promise in PAD applications, laying the groundwork for future research to address broader data privacy concerns and improve cross-dataset generalization. Code available here: https://gitlab.idiap.ch/bob/bob.paper.wacv2025_chatgpt_face_pad