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SHIELD<img src="logo/logo.png" alt="Logo" width="30" height="30">: An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
<p align="center"> <a href="https://img.shields.io/badge/version-v0.1.0-blue"> <img alt="version" src="https://img.shields.io/badge/version-v0.1.0-blue?color=FF8000?color=009922" /> </a> <a > <img alt="Status-building" src="https://img.shields.io/badge/Status-building-blue" /> </a> <a > <img alt="PRs-Welcome" src="https://img.shields.io/badge/PRs-Welcome-red" /> </a> <br /> </p> <p align="center"> <img src="logo/logo.png" style="width: 200px" align=center> </p>Overview
In this paper, we conduct a detailed comparison of two models: Google's Gemini <img src="logo/Gemini.png" alt="Gemini" width="30" height="30"> and OpenAI's GPT-4V(ision) <img src="logo/GPT-4V.png" alt="GPT-4V" width="30" height="30">. We utilize Zero-Shot/One-Shot as well as COT methods to comprehensively analyze the performance of these two models in FAS and Face Forgery Detection tasks.Meanwhile, we introduce a novel MCOT method, which has been empirically validated to significantly enhance detection accuracy.
Release
- [2024/2/7]🔥🔥🔥We released the evaluation result Arxiv Paper (about 100 pages) and the code.
🔗 Citation
If you find our work helpful, please cite:
@article{shi2024shield,
title={SHIELD: An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models},
author={Shi, Yichen and Gao, Yuhao and Lai, Yingxin and Wang, Hongyang and Feng, Jun and He, Lei and Wan, Jun and Chen, Changsheng and Yu, Zitong and Cao, Xiaochun},
journal={arXiv preprint arXiv:2402.04178},
year={2024}
}